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Why staying at home is so important

April 16, 2020

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The Coronavirus Crisis

Flattening a pandemic's curve: why staying home now can save lives.

Maria Godoy

Don't see the graphic above? Click here.

As the coronavirus continues to spread in the U.S., more and more businesses are sending employees off to work from home. Public schools are closing, universities are holding classes online, major events are getting canceled, and cultural institutions are shutting their doors. Even Disney World and Disneyland are set to close . The disruption of daily life for many Americans is real and significant — but so are the potential life-saving benefits.

It's all part of an effort to do what epidemiologists call flattening the curve of the pandemic. The idea is to increase social distancing in order to slow the spread of the virus, so that you don't get a huge spike in the number of people getting sick all at once. If that were to happen, there wouldn't be enough hospital beds or mechanical ventilators for everyone who needs them, and the U.S. hospital system would be overwhelmed. That's already happening in Italy.

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"If you think of our health care system as a subway car and it's rush hour and everybody wants to get on the car once, they start piling up at the door," says Drew Harris, a population health researcher at Thomas Jefferson University in Philadelphia. "They pile up on the platform. There's just not enough room in the car to take care of everybody, to accommodate everybody. That's the system that is overwhelmed. It just can't handle it, and people wind up not getting services that they need."

Harris is the creator of a widely shared graphic visualizing just why it is so important to flatten the curve of a pandemic, including the current one — we've reproduced his graphic at the top of this page. The tan curve represents a scenario in which the U.S. hospital system becomes inundated with coronavirus patients.

However, Harris says, if we can delay the spread of the virus so that new cases aren't popping up all at once, but rather over the course of weeks or months, "then the system can adjust and accommodate all the people who are possibly going to get sick and possibly need hospital care." People would still get infected, he notes, but at a rate that the health care system could actually keep up with — a scenario represented by the more gently sloped blue curve on the graph.

Singapore Wins Praise For Its COVID-19 Strategy. The U.S. Does Not

These two curves have already played out in the U.S. in an earlier age — during the 1918 flu pandemic. Research has shown that the faster authorities moved to implement the kinds of social distancing measures designed to slow the transmission of disease, the more lives were saved. And the history of two U.S. cities — Philadelphia and St. Louis — illustrates just how big a difference those measures can make.

In Philadelphia, Harris notes, city officials ignored warnings from infectious disease experts that the flu was already circulating in their community. Instead, they moved forward with a massive parade in support of World War I bonds that brought hundreds of thousands of people together. "Within 48, 72 hours, thousands of people around the Philadelphia region started to die," Harris notes. Within six months, about 16,000 people had died.

Meanwhile, officials in St. Louis, Mo., had a vastly different public health response. Within two days of the first reported cases, the city quickly moved to social isolation strategies, according to a 2007 analysis.

"They really tried to limit the travel of people and implement Public Health 101 — isolating and treating the sick, quarantining the people who have been exposed to disease, closing the schools, encouraging social distancing of people," Harris says. "And, of course, encouraging hand hygiene and other individual activities."

As a result, St. Louis suffered just one-eighth of the flu fatalities that Philadelphia saw, according to that 2007 research. But if St. Louis had waited another week or two to act, it might have suffered a fate similar to Philadelphia's, the researchers concluded.

At the time the 2007 research was released, Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases and a leading adviser in the U.S. response to COVID-19, the disease caused by the current coronavirus, said the evidence was clear that early intervention was critical in the midst of the 1918 pandemic.

As for just how big the current coronavirus pandemic will be in America? "It is going to be totally dependent upon how we respond to it," Fauci told Congress earlier this week.

"I can't give you a number," he said. "I can't give you a realistic number until we put into [it] the factor of how we respond. If we're complacent and don't do really aggressive containment and mitigation, the number could go way up and be involved in many, many millions."

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• Published: 02 June 2021

A global analysis of the impact of COVID-19 stay-at-home restrictions on crime

• Amy E. Nivette   ORCID: orcid.org/0000-0003-0597-3648 1 , 2 ,
• Renee Zahnow   ORCID: orcid.org/0000-0001-5796-9443 3 ,
• Raul Aguilar   ORCID: orcid.org/0000-0002-0541-4910 4 ,
• Andri Ahven 5 ,
• Shai Amram 6 ,
• Barak Ariel   ORCID: orcid.org/0000-0002-6912-2546 6 , 7 ,
• María José Arosemena Burbano 7 ,
• Roberta Astolfi 8 ,
• Dirk Baier 9 ,
• Hyung-Min Bark   ORCID: orcid.org/0000-0001-7848-4314 10 ,
• Joris E. H. Beijers 2 ,
• Marcelo Bergman 11 ,
• Gregory Breetzke   ORCID: orcid.org/0000-0002-0324-2254 12 ,
• I. Alberto Concha-Eastman   ORCID: orcid.org/0000-0002-7256-6164 13 ,
• Sophie Curtis-Ham   ORCID: orcid.org/0000-0001-8093-4804 14 ,
• Ryan Davenport 15 , 16 ,
• Carlos Díaz 17 ,
• Diego Fleitas   ORCID: orcid.org/0000-0001-8305-2057 11 ,
• Manne Gerell   ORCID: orcid.org/0000-0002-2145-113X 18 ,
• Kwang-Ho Jang 19 ,
• Juha Kääriäinen 20 ,
• Tapio Lappi-Seppälä   ORCID: orcid.org/0000-0003-1377-661X 20 ,
• Woon-Sik Lim 19 ,
• Rosa Loureiro Revilla 7 ,
• Lorraine Mazerolle   ORCID: orcid.org/0000-0002-3691-8644 3 ,
• Gorazd Meško 21 ,
• Noemí Pereda   ORCID: orcid.org/0000-0001-5329-9323 22 ,
• Maria F. T. Peres   ORCID: orcid.org/0000-0002-7049-905X 8 ,
• Rubén Poblete-Cazenave   ORCID: orcid.org/0000-0002-3954-1651 23 ,
• Simon Rose 7 , 16 ,
• Robert Svensson   ORCID: orcid.org/0000-0002-6080-2780 18 ,
• Nico Trajtenberg 24 ,
• Tanja van der Lippe 1 ,
• Joran Veldkamp 1 ,
• Carlos J. Vilalta Perdomo   ORCID: orcid.org/0000-0002-6030-7018 25 &
• Manuel P. Eisner   ORCID: orcid.org/0000-0001-5436-9282 7 , 26  

Nature Human Behaviour volume  5 ,  pages 868–877 ( 2021 ) Cite this article

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The stay-at-home restrictions to control the spread of COVID-19 led to unparalleled sudden change in daily life, but it is unclear how they affected urban crime globally. We collected data on daily counts of crime in 27 cities across 23 countries in the Americas, Europe, the Middle East and Asia. We conducted interrupted time series analyses to assess the impact of stay-at-home restrictions on different types of crime in each city. Our findings show that the stay-at-home policies were associated with a considerable drop in urban crime, but with substantial variation across cities and types of crime. Meta-regression results showed that more stringent restrictions over movement in public space were predictive of larger declines in crime.

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On 11 March 2020, the World Health Organization declared COVID-19 to be a public health emergency of global concern. Following the WHO declaration, national and local authorities moved to impose a range of measures to slow the spread of the virus (‘flatten the curve’) and alleviate strain on health care systems. Collectively referred to as ‘lockdown’ measures in most countries, regulations have included some combination of stay-at-home orders, travel bans, closures of schools and places of entertainment and restrictions on public and private gatherings. Strategies aimed at limiting the mobility of the entire population through measures that require or recommend that residents do not leave the house except for ‘essential’ activities arguably were among the most intrusive policies, with wide-ranging collateral effects on society, the economy and human rights 1 . Spatial mobility data suggest that, at the peak of the so-called lockdown—in late March and April 2020—daily movements related to retail and recreation had declined by over 80% in many countries in Europe and Latin America 2 .

In this study, we examine the extent to which stay-at-home restrictions in 27 cities in the Americas, Europe, the Middle East and Asia were associated with a change in levels of six types of police-recorded crime. The cities represent a large variation of measures relating to stay-at-home restrictions. They range from mostly voluntary recommendations to avoid public space (for example, Malmö and Stockholm in Sweden) to a complete halt of all but the most essential activities, based on emergency legislation and enforced by substantial penalties for breaching the rules (for example, Lima in Peru). This allows us to move significantly beyond previous studies conducted in single cities to evaluate the generalizability of criminogenic processes triggered by stay-at-home restrictions.

Various theories of crime examine how sudden and persisting restraints on population movements caused by, for example, natural disasters, blackouts or epidemics affect crime levels 3 . Theories of individual and structural strain suggest that such restraints increase levels of stress and negative emotions such as anxiety, frustration and anger, thereby leading to an increase in criminal motivations 4 . In this vein, social isolation and reduced freedom of movement associated with COVID-19 containment policies are anticipated to heighten levels of strain and reduce access to support with implications for child maltreatment 5 , domestic violence 6 and substance use 7 . Opportunity theory and routine activity theory, in contrast, suggest that stay-at-home policies interrupted the daily movements in time and space of suitable targets, capable guardians and motivated offenders on which most crime, especially crime in public space, feeds 8 . They hence predict that crime levels fall as the mobility of entire urban populations is restricted 9 .

While some early studies suggested that violent and non-violent crime dropped as regulations were imposed, there is also evidence that the effects of COVID-19 on crime are not universal across countries nor across different categories of crime 10 , 11 , 12 . Rather, opportunity structures are specific to different types of crimes, and a change in opportunities for theft may not correlate with a change in opportunities for assault 13 . For example, opportunities for certain property crimes, such as theft and robbery, depend on the daily flow of people into commercial areas and nearby transportation nodes that offer a high volume of suitable targets and access/exit paths for motivated offenders, and may hence have declined particularly strongly as a result of the lockdown measures 14 , 15 . Similarly, as most people stayed at home throughout the day, fewer houses were left unsupervised and residential burglary may have become much more difficult, while commercial buildings likely became less supervised and hence an easier target 9 . Also, while the shutdown of night-time leisure activities and alcohol consumption in urban centres greatly reduced the potential for violent conflict among young men in public spaces, the potential for domestic violence increased as victims found it harder to find help and support 16 . Finally, police services have also been required to adjust priorities and redistribute resources to carry out quarantine checks, enforce social distancing and enact border control 17 .

The impact of stay-at-home restrictions on crime

The COVID-19 pandemic and subsequent restrictions represent a series of ‘natural experiments’ in which population-wide changes affected routines, social interactions and the use of public space. An interrupted time series (ITS) design can then be used to assess the impact of the treatment while accounting for pre-COVID-19 crime trends 18 . ITS analyses provide an estimate of changes in levels of crime following an ‘interruption’ in the time series, while accounting for potential confounders such as long-term trends, autocorrelation and other time-varying confounders 19 . In an ITS analysis, the assumption is that, without the intervention (that is, COVID-19 restrictions), there would be no change in the pre-intervention trend 18 .

The dependent variable in our analyses is police-recorded daily reported crime incidents for six major crime categories: assault, theft, burglary, robbery, vehicle theft and homicide. To ensure that the crime categories were as comparable as possible across contexts, we utilized definitions from the International Classification of Crime for Statistical Purposes 20 for reference when collecting and aggregating crime data from each site (Supplementary Table 3 ). Not all crime categories were available for each city, and in some contexts certain crimes are not treated as separate categories. For example, in Seoul, burglary is not considered separately from robbery, and motor vehicle theft is not distinguished from theft. To ensure that the crime categories are as comparable as possible, we excluded these combined outcomes from the analyses (Supplementary Tables 4 – 10 ).

The treatment variable for the current analyses is a dummy variable defined by the date on which stay-at-home restrictions or recommendations were first implemented in each city, state/province or country (Supplementary Table 11 ). The effects of stay-at-home restrictions are modelled as a step function, whereby 0 represents the time period before and (if applicable) after the implementation of stay-at-home restrictions, and 1 represents the time period during stay-at-home restrictions. In this way, the step function estimates the extent to which the restrictions had an immediate effect on crime during the intervention period.

Given the count nature of our data, and the variation in frequency of daily crime incidents across cities (ranging from 0 to >500 daily incidents), in the present analyses we estimated Poisson generalized linear models using a logit-link function. This flexible approach provides an estimate of the level change in crime incidents after the implementation of stay-at-home restrictions. All tests are two-tailed, and models adjust for seasonality, autocorrelation and potential outliers. In addition, we included average daily temperature in Celsius as a covariate to account for potential fluctuations in crime due to higher temperatures 21 .

As an initial step, we conducted a series of descriptive analyses to evaluate the changes in crime before and after the implementation of COVID-19 stay-at-home restrictions. First, we calculated the average number of crimes in each city and category before and after the implementation of restrictions (Supplementary Table 15 ). Second, we plotted the 7-day moving average of daily crime counts for each city and crime. The moving average trend was indexed to equal 100 at the date on which the first stay-at-home restrictions were implemented. In this way, we can compare the direction of the trend immediately before and following restrictions across cities with different levels of crime. The mean trend for each type of crime was plotted alongside each city’s trend (Fig. 1 ). Supplementary Figs. 1 – 6 present the moving average trends broken down by city and type of crime. The full (non-smoothed) time series plots for each city and type of crime can be found in Supplementary Figs. 7 – 25 .

a, Assault ( n  = 23). b , Burglary ( n  = 20). c , Robbery ( n  = 24). d , Theft ( n  = 16). e , Vehicle theft ( n  = 20). f , Homicide ( n  = 25). Each time series is indexed to equal 100 on the day the first stay-at-home restrictions were implemented. The blue line indicates the average trend across all cities with available data. Zero time is the date on which stay-at-home restrictions were implemented.

The descriptive results suggest that stay-at-home restrictions are associated with declines in all types of crime, with the exception of homicide. In Barcelona, for example, police-recorded thefts declined from an average of 385.2 to 38.1 per day (Supplementary Table 15 ). However, there still appears to be substantial variation across crime categories and cities in the size and direction of crime trends following the implementation of restrictions. Over time, the mean trend begins to return to pre-treatment levels of crime.

Next, we estimated the size of the level change in daily crimes that can be attributed to stay-at-home restrictions using ITS analysis. The analyses of trends for six categories of police-recorded daily reported crime incidents across 27 cities result in over 100 estimates of effect. To summarize this information, we used meta-analytical techniques to estimate the grand mean effect of stay-at-home restrictions for each type of crime (Table 1 ). The estimates of effect, expressed as the incidence rate ratio (IRR) with 95% confidence interval, are presented in Figs. 2 and 3 for violent and property crimes, respectively. The high number of hypotheses tested increases the possibility that we may detect a significant result due to chance. We therefore urge caution in interpreting the results for individual cities. The breakdown of effect sizes and summary effects by city are available in Supplementary Table 16 . Across our sample, crime declined overall by 37% following the implementation of stay-at-home restrictions.

a , Assault ( n  = 23). b , Robbery ( n  = 24). c , Homicide ( n  = 21). Overall summary effects estimated using random-effects meta-analytic techniques. ES, effect size. SaH (days), number of days under stay-at-home restrictions from the beginning of 2020 until the end of the respective time series from May to September 2020. Full results by city and crime can be found in Supplementary Table 17 .

a , Burglary ( n  = 20). b , Theft ( n  = 16). c , Vehicle theft ( n  = 20). Overall summary effects estimated using random-effects meta-analytic techniques. ES, effect size. SaH (days), number of days under stay-at-home restrictions from the beginning of 2020 until the end of the respective time series from May to September 2020. Full results by city and crime can be found in Supplementary Table 17 .

For assault, the summary effect suggests that the implementation of stay-at-home restrictions was associated with a 35% reduction in daily assaults (Fig. 2a ). The I 2 value of 98.4% suggests substantial heterogeneity in the effect sizes across cities and crime outcomes (Table 1 ). Similarly, effect sizes for robbery vary across cities, however no cities experienced a statistically significant increase in the number of daily robbery incidents following restrictions. The average size of the level change following restrictions was 46%.

The results for homicide suggest that overall there was a marginal decline in the number of daily homicides following the implementation of stay-at-home restrictions (14%, Fig. 2c ). However, only three cities (Lima, Cali and Rio de Janeiro) saw a statistically significant decline in homicides. The I 2 statistic (54.6%, Table 1 ) indicates relatively less heterogeneity in effects compared with other crime outcomes.

The distribution of effect sizes for burglary ranges from an 84% decline (Lima) to a 38% increase (San Francisco) in the number of daily incidents. The summary effect is relatively smaller compared with assault, where on average burglaries fell by 28% following the implementation of restrictions.

All cities with available data on theft experienced a significant drop in the number of daily incidents, however the I 2 statistic (99.2%) still indicates high levels of heterogeneity between cities. Even cities with less restrictive, more voluntary stay-at-home recommendations (for example, Malmö and Stockholm) experienced marginal declines in the number of daily thefts. The results for vehicle theft also suggest heterogeneity in effects across cities, with 8 out of 18 cities experiencing no statistically significant change in the number of incidents following restrictions. The mean drop in vehicle thefts across cities was 39%.

Stringency of restrictions and size of decline

The next step is to evaluate why we find such substantial heterogeneity in effect sizes across cities. Heterogeneity in effect sizes can be attributed to, for example, variations in the characteristics of local or national policies. We estimate the extent to which variations in effect sizes are associated with the stringency of stay-at-home restrictions and wider COVID-19-related containment policies. To measure stringency, we drew from the Oxford Government Response Tracker documentation and coding of COVID-19 policy responses 22 . The stringency of stay-at-home restrictions is measured on a scale from 0 (no measures) to 3 (do not leave the house with minimal exceptions).

For the current analyses, we took the average of the stay-at-home scores between the first day of implementation to either the lifting of restrictions or the end of the time series, whichever came first (Supplementary Table 11 ). Using mixed-effects meta-regression techniques, we are able to assess whether more severe restrictions on routine activities are associated with greater declines in daily crimes (that is, larger negative effect sizes).

The results in Table 2 suggest that more stringent stay-at-home restrictions were associated with significantly more negative effect sizes for burglary, robbery, theft and vehicle theft. In essence, this suggests that more severe restrictions on ‘non-essential’ movement and activities were associated with significantly larger declines in crime. While the coefficients are negative for assault, the association was not significant at the conventional threshold of 0.05. However, inspection of the scatterplots suggests that Barcelona may be an outlier (Supplementary Fig. 26 ). When Barcelona is excluded from meta-regression analyses, more stringent stay-at-home restrictions are negatively associated with effect sizes for assault (Supplementary Table 19 ). The adjusted R 2 values for burglary and robbery show that the stringency of stay-at-home restrictions accounts for about 35% of the variation in effect sizes across cities.

Additional analyses

As an additional set of analyses, we evaluated the possibility that other COVID-19-related policy responses may account for variations in the size of the effect instead of, or in addition to, stay-at-home restrictions. For example, based on strain perspectives, we may expect smaller declines in cities and contexts where there is less economic support for those affected by unemployment or financial strain due to the pandemic. This would be because individuals experiencing strain are motivated to cope by seeking out alternative, possibly illegal income opportunities. In addition, since stay-at-home restrictions were often implemented alongside a wide range of policies that regulated leisure activities, routines and opportunities, we also examined the relationship between the overall stringency index and effect sizes for each type of crime.

The results show that more severe restrictions on school opening, working from home, public events, private gatherings and internal movement are not significantly related to the size of effects (Supplementary Table 21 ), with one exception: More stringent reductions or closures of public transportation are associated with more negative effect sizes for robbery and vehicle theft only. More economic support was not associated with the variation in effect sizes for any type of crime. The results for the overall stringency index were generally in line with the main findings for stay-at-home restrictions, whereby more stringent combinations of containment policies were associated with greater declines in burglary, robbery, vehicle theft and theft. However, comparing the model fit (adjusted R 2 values) suggests that accounting for the overall combined policy response does not substantially improve the model fit.

Further, while the stringency indices and sub-indices provide systematic and comparable measures of COVID-19 containment policies across countries, they do not provide a measure of actual behavioural changes. We therefore conducted additional analyses to assess the relationship between changes in mobility indices as measured by the Google COVID Community Mobility Reports 23 , 24 , and effect sizes for each type of crime. Bivariate correlations between mobility measures and stringency measures suggest that more stringent stay-at-home restrictions are associated with greater declines in visits to commercial locations and parks, as well as increases in users remaining in their residences (Supplementary Table 14 ). The results using mobility indices are generally in line with the results using the stringency index measures, whereby cities that saw greater declines in the use of public space saw larger declines in crime, with the exception of homicide (Supplementary Table 22 ).

In this study, we examined trends in police-recorded crime in the period after the introduction of stay-at-home policies in 27 cities worldwide. Our findings show that the stay-at-home policies were associated with a substantial drop in urban crime. On average, the overall reduction in crime levels across all included cities was −37%. They suggest that the sudden decline in urban mobility triggered by the stay-at-home policies reduced opportunities and increased guardianship relating to many high-volume crimes. In other words, as expected by economic and criminological opportunity theories, we found strong evidence that crime levels respond quickly to changing opportunity structures and constraints, and that change in crime levels does not necessarily require large-scale changes in offender motivation 15 . At least in the short run, the change in routine activities rather than the increase in psychological and social strains was the dominating mechanism that affected change in overall crime levels. We did not find evidence for or against displacement effects in the sense of a shift from one type of crime to another within the categories of crime covered in this paper. However, the lack of high-quality comparable data means that we could not examine the possibility that a substantial amount of coercive and property crime moved online, parallel to the sudden shift in daily routine activities.

Visual inspection of crime trends anchored by the beginning of the stay-at-home orders suggests that the declines were short-lived, with a maximum drop around two to five weeks after the implementation of the measures and a gradual return to previous levels in the subsequent weeks (Fig. 1 ). This aligns with previous research conducted in Australia 25 and China 26 that found that immediate declines in public-space crimes such as theft, burglary and traffic offences experienced during lockdown periods quickly reversed as restrictions eased. Future research should examine whether these longer trends in crime levels reflect the gradual relaxation of the constraints during June, July and August of 2020, or whether they rather signal the slower build-up of strains due to the social and economic disruption experienced especially by disadvantaged young people.

Across cities, the rapid deceleration of urban activity had comparable effects on similar crime categories, despite variation in size, geographic location and social structure. The average reduction was smallest for homicide with −14%. It was largest for robbery and theft with −46% and −47% respectively, with the reductions for burglary (−28%), vehicle theft (−39%) and assault (−35%) in between. We observe the largest effects for crimes that involve the convergence of motivated offenders and suitable victims/targets in public space, likely because far fewer potential victims spent time in crime hotspots such as inner-city areas with concentrations of businesses and entertainment venues 2 . Also, efforts to monitor compliance with stay-at-home regulations probably increased levels of formal social control in urban space 9 .

In contrast, reductions were much more limited for homicide. The smaller decrease in homicide cases may be due to a number of factors. First, in many societies, a substantial proportion of homicides are committed in domestic contexts and are hence not affected by the reduction in the density of daily encounters in cities. Second, a varying proportion of homicide is associated with organized crime, conflicts between gangs or conflicts related to drug trafficking. The behaviour of these groups may be less elastic to changes in the daily routines of those not involved in organized crime. In this vein, conventional crimes in Mexico City declined while crimes associated with organized crime (homicide, extortion and kidnappings) did not 12 . However, this argument does not always hold. More specifically, in three of the studied cities (Cali, Lima and Rio de Janeiro), a large proportion of homicides are committed by gang members. However, homicide levels dropped substantially in each city after the stay-at-home orders. One possible explanation is that criminal groups used the crisis to strengthen their power by imposing their own curfews and restricting movement in the territories they control 27 .

The reduction in burglaries is likely related to increased informal social control in that more dwellings were occupied around the clock, hence offering fewer opportunities for burglaries with a low risk of being disturbed. However, we note that, for many cities, it was not possible to distinguish residential and commercial burglaries. Additional analyses for cities where such a separation is possible suggest that, in line with these arguments, commercial burglaries were largely unaffected by the stay-at-home orders while burglaries against private premises were more likely to decline (Supplementary Fig. 28 ).

Finally, we examined whether variation in the stringency of the lockdown was predictive of the amount of change in crime. Results show that more stringent limitations regarding requirements/recommendations to stay at home were associated with stronger declines in crime levels (Fig. 4 ). The additional analyses suggest that it is mostly the stay-at-home requirements that were associated with larger declines, in that other containment policies were generally not significantly associated with declines, and the use of the overall stringency index generally did not substantially improve the models (Supplementary Table 21 ). We found few systematic differences in the ‘elasticity’ of different crime categories, that is, in the extent to which variation in the stringency of COVID-19-related restrictions was associated with change in crime levels. This suggests, surprisingly perhaps, that all crime categories included in this analysis responded similarly to variation in the extent of constraints on daily movement.

The overall average decline was computed using the summary effect sizes for each city reported in Supplementary Table 17 , with predicted linear relationship between average decline and stay-at-home stringency index (dotted line and shaded area) and 95% confidence intervals (shaded area).

One must bear in mind that the comparative focus of the present analyses made it impossible, for example, to conduct more fine-grained analyses by contextual characteristics. We would expect, for example, that a distinction of assault cases by place would reveal that assault in the hotspots of weekend night-time activities declined more where the lockdown was more stringent, while violence in domestic contexts may not have declined or may even have increased. Our results might be hiding a more complex picture characterized by neighbourhood heterogeneity in terms of both the independent and the dependent variables. Research in Chicago shows that there is heterogeneity in the impact of containment policies across communities and only a small percentage of communities experienced significant reductions in crime. Variation depended on the type of crime (for example, burglaries, assaults, narcotic-related offences and robberies), community crime characteristics (for example, previous levels of offences, perception of safety and presence of police station) and socioeconomic characteristics (vacant housing, income diversity, poverty, age structure of neighbours and self-perceived health of neighbours) 28 , 29 . What is more, research in India has shown that higher stringency of lockdown restriction across city districts is associated with lower levels of economically motivated crimes and higher levels of violence against women 30 . Further research on variations within cities and at micro-places is needed to provide further insights into the moderating effect of local contexts on the effects of COVID-19 restrictions on crime.

While the results presented here extend knowledge on the impact of COVID-19 restrictions on crime across international contexts, the study is not without limitations. We acknowledge that the sample of cities included in the analyses is non-random and dominated by cities situated within Europe and the Americas. Further, relying on officially recorded crime data is associated with issues of under-reporting and variations in crime definitions and operational priorities. Police records have well-known problems of reporting/recording, which depends on the type of crime, willingness of victims to report, how criminal justice and health agencies work and their institutional practices, which might be heterogeneous and particularly more problematic in low- and middle-income societies 31 . These measurement problems might be more accentuated during the pandemic given that it might affect victims’ willingness to report crimes 32 . Also, police responses to crime might also change because of staff absences due to COVID-19, increased fear of contracting the virus or even diversion of police resources to alternative tasks such as enforcing the lockdown 25 , 30 , 33 . However, studies that use alternative sources have partially validated our results. A recent study in Wales used emergency department visits for violence-related injuries to show that lockdown measures had an impact on the decrease of violence outside the home while no significant differences were observed in violent events at home 34 . We also acknowledge that identifying the specific policy components that affected crime levels remains a challenge in macro-level comparative analyses. Across countries, a range of measures that affect the daily movement of citizens were implemented broadly at the same time. Our analyses suggest that stay-at-home policies played a crucial role. However, more fine-tuned analyses would be needed to understand the extent to which other measures (for example, closing bars, limiting public transport and closing schools) and variation in their enforcement were associated with variation in crime trends across places within a city.

An important area for future comparative research is to investigate the potential displacement of public-space crimes to non-contact offences such as fraud and cybercrime, which we were unable to measure here. Studies conducted within the context of individual countries provide some evidence of displacement from public-space crimes to domestic violence 32 , 35 . There is some initial evidence of a significant increase of cybercrime during the strictest period of lockdown in the United Kingdom, which is interpreted as a displacement of crime opportunities from the offline to the online environment 36 . Restrictions on public space may have also led to displacement of crime to private space. A recent meta-analysis by Piquero and colleagues suggests that there is strong evidence showing an increase of domestic violence during the pandemic using studies with multiple sources other than police reports (for example, emergency hotline registries, health records and other administrative documents) 32 . This suggests that future research should consider the impact of restriction stringency across cities and countries on the extent of shifts in crime from the public to the domestic sphere.

Finally, it is important to emphasize that the impact of COVID-19-related containment policies on crime trends must be considered within the broader context of global declines in some types of crime, including homicide 37 , 38 , 39 , 40 , 41 and vehicular theft 42 , allied with increases in technology-facilitated offences and the potential accelerating effect of the pandemic on this trend.

The measures taken by governments across the world to control COVID-19 continue to have a profound impact on all aspects of social life. They are an opportunity to add to our understanding of social processes, including those involved in the causation of city-wide crime levels. As the crisis progresses, cities and countries continuously adapt their public health strategies. A crucial next step will be to examine longer-term dynamics in more cities globally. Also, we need to complement the comparative macro-level analyses presented here with analyses of how the control measures have affected crime trends in micro-contexts such as crime hotspots.

Daily crime data were collected from 27 cities representing 23 countries around the world. The cities were selected in an attempt to maximize geographical coverage and capture a range of policy responses aimed to reduce the transmission and spread of COVID-19. We sought daily crime data on assault, burglary, robbery, theft, vehicle theft and homicide for the current analyses.

Attempts were made to gather daily data from Guatemala, Jamaica, Romania, Norway, Italy, Jordan, South Africa, Ghana, India, the Philippines, Taiwan, China and Japan. However, data were not accessible due to non-response, unavailability of data at a daily interval or refusal. Data for San Francisco, Chicago, Vancouver, Toronto, Muzaffarpur, Brisbane, Auckland, Mendoza and Mexico City were publicly available on police department or city websites 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 .

Data for Mendoza refer to Mendoza Province, in which the majority of the population reside in the metropolitan area of Mendoza. Daily data in Mendoza Province are collected primarily from the metropolitan area for technical and procedural reasons, meaning that the data largely reflect the urban population in the province. Data for Muzaffarpur refer to Muzaffarpur District, in which roughly 9% (473,000) live in urban areas within the district with the remaining population residing in rural areas 52 . The data for Zürich refer to the cantonal territory, which is predominantly urban. Additional information regarding sources and definitions can be found in Supplementary Tables 2 – 9 .

The ‘date’ of the time series refers to the date the offence presumably occurred, as recorded by the police. In cases where this information was not available (that is, Amsterdam and São Paulo), the date of reporting was used. In Mexico City, observations refer to the number of criminal investigations initiated. Since not all reported crimes are investigated, in this case the number may under-represent the volume of crime reported to the police. For most cities, the time series starts on 1 January 2018 or 2019 and ends on the most recent date available. Time series information and available crime categories for each city are presented in Supplementary Table 10 .

The ‘treatment’ variable

Covid-19 stay-at-home restrictions.

The treatment effect of a city’s stay-at-home restrictions on crime incidents is measured using a dummy variable, whereby 1 represents the period in which restrictions were in place and 0 represents the period prior to (or following) the implementation of restrictions.

The date on which restrictions or recommendations were implemented is not always clear-cut across cities. In some cases, restrictions were implemented piecemeal on a local level over time, and in other cases policies were implemented nationwide at once. In these less clear-cut cases, we relied on information from our local collaborators, complemented by information from the Oxford COVID-19 Government Response Tracker 22 as well as local media resources. Supplementary Table 11 provides summary information including the start and (where relevant) end date of the COVID-19 responses for each city, with a focus on stay-at-home restrictions.

Covariates in ITS models

Climate data for cities were drawn from the National Centers for Environmental Information 53 . Where information was missing for certain cities and dates, we manually extracted data from Weather Underground ( www.wunderground.com ). Climate data for Lima, Peru were not available from 1 January 2018 to May 2018.

In addition, we include yearly population as an offset in all models. Population data for each city were drawn from the United Nations’ World Population Prospects 54 . Population data for Ljubljana, Tel Aviv-Yafo and Guayaquil were drawn, respectively, from the Republic of Slovenia’s Statistical Office 55 , the Israel Central Bureau of Statistics 56 and the National Institute of Statistics and Censuses of Ecuador 57 , respectively. Projected population data for Muzaffarpur were drawn from the IndiaGrowing website 52 .

Interrupted time series analyses

The ITS analyses were estimated using Poisson generalized linear models with a logit-link function. An important potential confounder in ITS stems from seasonal or long-term trends 18 , 19 , 58 , 59 , 60 , 61 . Seasonality can typically be visually identified by cyclical patterns 58 . For daily crime data, there are several potential seasonal patterns that must be addressed. Crime patterns have been found to increase periodically during summer months 59 , and certain crimes, such as assaults, are more likely to occur during weekends compared with weekdays 60 . Based on visual inspection of the time series plots, we controlled for seasonal trends using a series of dummy variables representing month of the year, week of the year and/or day of the week 58 , 59 , 60 , 61 .

Another methodological issue to address in ITS analyses is autocorrelation. Autocorrelation refers to the similarity between two observations, which violates the assumption of independence 58 . It is possible to identify systematic patterns of autocorrelation between residuals, which can then be accounted for within the regression model for more accurate estimation of effects 62 . Two common models refer to autoregressive processes and moving average processes. An autoregressive process identifies correlations between lagged observations and is modelled by including past values of the outcome in the regression model 58 , 62 . A moving average process refers to systematic patterns in the residuals, which can be modelled by including terms for relevant lagged residuals into the regression model 58 .

Patterns of residual autocorrelation were evaluated by inspecting the partial autocorrelation function and autocorrelation function plots. Once any autoregressive and moving average processes were identified and accounted for in the model, these plots, as well as multivariate portmanteau (Q) statistics, were used to determine the extent to which the residuals were ‘white noise’, meaning all processes have been accounted for and there is no significant, systematic autocorrelation between the residuals 63 , 64 . When two models fitted similarly well, we chose for the more parsimonious model with the lowest Akaike information criterion value.

In addition to the above methodological issues, we took several steps to improve the estimation model and fit for each time series. Prior to modelling, a Dickey–Fuller test was used to test for non-stationarity in the time series. Any outliers, defined by significant spikes or dips, were modelled using dummy variables. This includes any holidays where crime incidents are likely to be higher (for example, carnival) or lower (for example, Christmas holidays and New Year’s Day) than usual. Following recommendations, we also included a scaling adjustment to each model to correct for over-dispersion and more accurately estimate standard errors 19 . All models included an offset for population by year.

In some cities, the frequency of crimes per day was almost zero, drawing into question the approriateness of conducting daily time series analyses. This occurred most often for homicide (in Brisbane, Helsinki, Ljubljana, Tallinn and Vancouver), where the daily average number of incidents ranged from 0 to 0.038. The number of assault incidents in Ljubljana during the time period was also near 0. When incidents are sparse, the model may become unstable and unreliable 65 . As such, we opted to exclude these cases from time series analyses.

Additional information on the ITS models can be found in the Supplementary Materials .

Meta-analyses

Due to the heterogeneous nature of lockdowns and crime definitions across countries, we used random-effects models to estimate summary effects. The random-effects approach assumes that effects vary in part due to characteristics of the treatment effect 66 . In this case, the stay-at-home restrictions varied considerably by the content and implementation of policies, as well as enforcement, which may impact the size of the effect. Random-effects models allow for a distribution of ‘true’ effects whereby the summary effect reflects the mean of this distribution 66 , 67 . The I 2 statistic displays the percentage of variation that can be attributed to heterogeneity, whereby values above 75% indicate high heterogeneity between results 67 , 68 .

Meta-regression

Covid-19 policy variables.

To examine the factors associated with the size of the effect of stay-at-home restrictions on crime trends, we utilized the Oxford Government Response Tracker’s coding of containment and economic policies 69 . Our focal analyses used the stringency of stay-at-home restrictions, measured as 0 for no measures, 1 for recommendations to not leave the house, 2 for requirements to not leave the house except for ‘essential’ activities and 3 for requirements to not leave the house with minimal exceptions.

In a meta-regression, the dependent variable is the estimated effect size for each city and crime category. In a mixed-effects meta-regression, two error terms are included in the estimation equation: the first is attributable to sampling error, and the second error is associated with deviation from the distribution of ‘true’ effect sizes 66 , 70 . Due to the small number of effect sizes included in each model, we estimated the effects of each policy variable separately.

Sensitivity analyses

A series of analyses were conducted to assess the sensitivity of meta-regression results to issues related to the operationalization of COVID-19 policy restrictions, outliers and differing definitions or categorizations of crimes across countries. Specifically, we assessed the extent to which results are sensitive to the use of cities with all available data (Supplementary Table 18 ), the exclusion of potential outliers (Supplementary Table 19 ) and the exclusion of cities where domestic or family assault is not distinguished from non-domestic assault incidents in police data (Supplementary Table 20 ).

Policy variables and mobility indices

As additional analyses, we examined six separate containment policies, the overall stringency index, economic support policies and Google COVID-19 mobility indices in the meta-regressions. Descriptive statistics for each policy and index for all available cities are available in Supplementary Tables 12 and 13 . This allows us to evaluate the extent to which the variations in the size of the effect can be attributed to the stringency of stay-at-home policies compared with other forms of containment or the combination of containment policies, including the stringency of school closures, workplace closures, restrictions on public events, restrictions on private gatherings, restrictions on public transportation and restrictions on internal travel (Supplementary Table 21 ). The COVID-19 mobility indices include measures of change corresponding to a number of public and private places, including retail and recreation, grocery and pharmacy, parks, workplaces, transit stations and residential places (Supplementary Table 22 ).

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All data used in the analyses have been deposited in the Data Archiving and Networked Services data repository ( https://doi.org/10.17026/dans-xuf-a75p ) for purposes of reproducing or extending the analysis.

Code availability

All statistical code used in the analyses has been deposited in the Data Archiving and Networked Services data repository ( https://doi.org/10.17026/dans-xuf-a75p ) for purposes of reproducing or extending the analysis.

Haug, N. et al. Ranking the effectiveness of worldwide COVID-19 government interventions. Nat. Hum. Behav. 4 , 1303–1312 (2020).

Article   Google Scholar  

Ritchie, H. Google mobility trends: how has the pandemic changed the movement of people around the world? Our World in Data https://ourworldindata.org/covid-mobility-trends (2020).

Hodgkinson, T. & Andresen, M. A. Show me a man or a woman alone and I'll show you a saint: changes in the frequency of criminal incidents during the COVID-19 pandemic. J. Crim. Justice 69 , 101706 (2020).

Agnew, R. Pressured into Crime: An Overview of General Strain Theory (Roxbury, 2006).

Griffith, A. K. Parental burnout and child maltreatment during the COVID-19 pandemic. J. Fam. Violence https://doi.org/10.1007/s10896-020-00172-2 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Bradbury-Jones, C. & Isham, L. The pandemic paradox: the consequences of COVID-19 on domestic violence. J. Clin. Nurs. 29 , 2047–2049 (2020).

Jemberie, W. B. et al. Substance use disorders and COVID-19: multi-faceted problems which require multi-pronged solutions. Front Psychiatry 11 , 714 (2020).

Bernasco, W., Ruiter, S. & Block, R. Do street robbery location choices vary over time of day or day of week? A test in Chicago. J. Res Crime. Delinq. 54 , 244–275 (2017).

Mohler, G. et al. Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis. J. Crim. Justice 68 , 101692 (2020).

Payne, J. L., Morgan, A. & Piquero, A. R. COVID-19 and social distancing measures in Queensland, Australia, are associated with short-term decreases in recorded violent crime. J. Exp. Criminol. https://doi.org/10.1007/s11292-020-09441-y (2020).

Eisner, M. & Nivette, A. Violence and the pandemic. Urgent questions for research. HFG Research and Policy in Brief https://www.hfg.org/violence-and-the-pandemic (2020).

de la Miyar, J. R. B., Hoehn-Velasco, L. & Silverio-Murillo, A. Druglords don’t stay at home: COVID-19 pandemic and crime patterns in Mexico City. J. Crim. Justice , 101745 (2020).

Kinney, J. B., Brantingham, P. L., Wuschke, K., Kirk, M. G. & Brantingham, P. J. Crime attractors, generators and detractors: land use and urban crime opportunities. Built Environ. 34 , 62–74 (2008).

Andresen, M. A. & Linning, S. J. The (in)appropriateness of aggregating across crime types. Appl. Geogr. 35 , 275–282 (2012).

Caminha, C. et al. Human mobility in large cities as a proxy for crime. PLoS ONE 12 , e0171609 (2017).

Stickle, B. & Felson, M. Crime rates in a pandemic: the largest criminological experiment in history. Am. J. Crim. Justice 45 , 525–536 (2020).

White, M. D. & Fradella, H. F. Policing a pandemic: stay-at-home orders and what they mean for the police. Am. J. Crim. Justice 45 , 702–717 (2020).

Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I. & Reeves, D. Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. Br. Med. J. 350 , h2750 (2015).

Bernal, J. L., Cummins, S. & Gasparrini, A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J. Epidemiol. 46 , 348–355 (2017).

PubMed   Google Scholar  

Bisogno, E., Dawson-Faber, J. & Jandl, M. The International Classification of Crime for Statistical Purposes: a new instrument to improve comparative criminological research. Eur. J. Criminol. 12 , 535–550 (2015).

Schinasi, L. H. & Hamra, G. B. A time series analysis of associations between daily temperature and crime events in Philadelphia, Pennsylvania. J. Urban Health 94 , 892–900 (2017).

Hale, T. et al. A global panel database of pandemic policies (Oxford COVID-19 government response tracker). Nat. Hum. Behav. https://doi.org/10.1038/s41562-021-01079-8 (2021).

Article   PubMed   Google Scholar  

Google COVID-19 community mobility reports. Google, Inc. https://www.google.com/covid19/mobility/index.html?hl=en (2020).

Mobility report CSV documentation. Google, Inc. https://www.google.com/covid19/mobility/data_documentation.html?hl=en (2020).

Andresen, M. A. & Hodgkinson, T. Somehow I always end up alone: COVID-19, social isolation and crime in Queensland, Australia. Crime Sci. 9 , 1–20 (2020).

Borrion, H., Kurland, J., Tilley, N. & Chen, P. Measuring the resilience of criminogenic ecosystems to global disruption: a case-study of COVID-19 in China. PLoS ONE https://doi.org/10.1371/journal.pone.0240077 (2020).

Berg, R. & Vasori, A. COVID-19 is increasing the power of Brazil’s criminal groups. LSE Latin America and Caribbean Blog https://blogs.lse.ac.uk/latamcaribbean/2020/05/28/covid-19-is-increasing-the-power-of-brazils-criminal-groups (2020).

Campedelli, G. M., Favarin, S., Aziani, A. & Piquero, A. Disentangling community-level changes in crime trends during the COVID-19 pandemic in Chicago. Crime Sci. 9 , 1–18 (2020).

Andresen, M.A. & Hodgkinson, T. in Revitalizing Victimization Theory: Revisions, Applications, and New Directions (eds. Pratt, T.C. & Turanovic, J.J.) 104–130 (Routledge, 2021).

Poblete-Cazenave, R. The Great Lockdown and criminal activity: evidence from Bihar. India Covid Econ. 29 , 141–163 (2020).

Google Scholar  

Guerrero, R., Gutiérrez, M. I., Fandiño-Losada, A. & Cardona, S. Sistema de indicadores comparables de convivencia y seguridad ciudadana: un consenso de países. Rev. Panam. Salud Pública 31 , 253–259 (2012).

Piquero, A., Jennings, W., Jemison, E., Kaukinen, C., & Knaul, F. Domestic violence during COVID-19: evidence from a systematic review and meta-analysis. Council on Criminal Justice https://cdn.ymaws.com/counciloncj.org/resource/resmgr/covid_commission/Domestic_Violence_During_COV.pdf (2021)

Halford, E., Dixon, A., Farrell, G., Malleson, N. & Tilley, N. Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime. Crime Sci. 9 , 11 (2021).

Shepherd, J., Moore, S., Mercer Kollar, L. & Sumner, S. Association between COVID-19 lockdown measures and emergency department visits for violence-related injuries in Cardiff, Wales. JAMA 325 , 885–887 (2021).

Article   CAS   Google Scholar  

Evans, M. L., Lindauer, M. & Farrell, M. E. A Pandemic within a pandemic—intimate partner violence during Covid-19. N. Engl. J. Med. 383 , 2302–2304 (2020).

Buil-Gil, D., Miró-Llinares, F., Moneva, A., Kemp, S. & Díaz-Castaño, N. Cybercrime and shifts in opportunities during COVID-19: a preliminary analysis in the UK. Eur. Soc. 23 , S47–S59 (2021).

Rennó Santos, M., Testa, A., Porter, L. C. & Lynch, J. P. The contribution of age structure to the international homicide decline. PLoS ONE 14 , e0222996 (2019).

Blumstein, A. & Wallman, J. The Crime Drop in America (Cambridge Univ. Press, 2006).

Farrell, G., Tseloni, A. & Tilley, N. The effectiveness of car security devices and their role in the crime drop. Criminol. Crim. Justice 11 , 21–35 (2011).

Tseloni, A., Mailley, J., Farrell, G. & Tilley, N. Exploring the international decline in crime rates. Eur. J. Criminol. 7 , 375–394 (2010).

Zimring, F.E. The Great American Crime Decline (Oxford Univ. Press, 2007).

Hodgkinson, T., Andresen, M. A. & Farrell, G. The decline and locational shift of automotive theft: a local level analysis. J. Crim. Justice 44 , 49–57 (2016).

San Francisco Police Department incident reports: 2018 to present. DataSF https://data.sfgov.org/Public-Safety/Police-Department-Incident-Reports-2018-to-Present/wg3w-h783 (2020)

Crimes – 2001 to present . Chicago Data Portal https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-Present/ijzp-q8t2 (2020)

Crime data download. Vancouver Police Department https://geodash.vpd.ca/opendata/ (2020)

Toronto Police Service Public Safety Data Portal https://data.torontopolice.on.ca/search?q=crime (2020)

State Crimes Records Bureau Bihar http://scrb.bihar.gov.in/View_FIR.aspx (2020)

Online crime map. Queensland Police Service https://qps-ocm.s3-ap-southeast-2.amazonaws.com/index.html (2020).

Daily occurences of crime and family violence investigations. New Zealand Police https://www.police.govt.nz/about-us/statistics-and-publications/data-and-statistics/daily-occurrences-crime (2020).

DataMendoza. Análisis de delitos seleccionados para el Centro de Estudios Latinoamericanos sobre Inseguridad y Violencia. Report, May 2020 (Ministerio de Seguridad, 2020).

DataCDMX. Portal de Datos Abiertos de la Ciudad de Mexico (CDMX). Gobierno de la Ciudad de Mexico https://datos.cdmx.gob.mx/dataset/victimas-en-carpetas-de-investigacion-fgj (2020)

Muzaffarpur district population. IndiaGrowing http://www.indiagrowing.com/Bihar/Muzaffarpur_District (2020)

National Centers for Environmental Information data access. National Oceanic and Atmospheric Administration https://www.ncdc.noaa.gov/data-access (2020)

World Population Prospects 2019. UN Department of Economic and Social Affairs, Population Division https://population.un.org/wpp (2020)

Selected data on municipalities, Slovenia, annually. Republic of Slovenia Statistical Office https://pxweb.stat.si/SiStatData/pxweb/en/Data/Data/2640010S.px/ (2020)

2019 population in localities. Israel Central Bureau of Statistics https://www.cbs.gov.il/he/publications/doclib/2017/population_madaf/population_madaf_2019_1.xlsx (2019)

Proyecciones poblacionales. Instituto Nacional de Estadística y Censos de Ecuador https://www.ecuadorencifras.gob.ec/proyecciones-poblacionales/ (2020)

Shin, Y., Time Series Analysis in the Social Sciences: the Fundamentals (Univ. of California Press, 2017).

McDowall, D., Loftin, C. & Pate, M. Seasonal cycles in crime, and their variability. J. Quant. Criminol. 28 , 389–410 (2012).

Andresen, M. A. & Malleson, N. Intra-week spatial–temporal patterns of crime. Crime Sci. 4 , 1–11 (2015).

Linning, S. J., Andresen, M. A. & Brantingham, P. J. Crime seasonality: examining the temporal fluctuations of property crime in cities with varying climates. Int J. Ther. Comp. Criminol. 61 , 1866–1891 (2017).

Hyndman, R. J. & Athanasopoulos, G. Forecasting: Principles and Practice (OTexts, 2018).

Dugan, L. in Handbook of Quantitative Criminology (eds. Piquero, A.R. & Weisburd, D.) 741–763 (Springer, 2010).

Sperling, R. & Baum, C. F. Sts19: multivariate portmanteau (Q) test for white noise. Stata Tech. Bull. Repr. 10 , 373–375 (2001).

Dugan, L. The series hazard model: an alternative to time series for event data. J. Quant. Criminol. 27 , 379–402 (2011).

Borenstein, M., Hedges, L. V., Higgins, J. P. & Rothstein, H. R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1 , 97–111 (2010).

Harris, R. J. et al. Metan: fixed-and random-effects meta-analysis. Stata J. 8 , 3–28 (2008).

Higgins, J. P., Thompson, S. G., Deeks, J. J. & Altman, D. G. Measuring inconsistency in meta-analyses. Br. Med. J. 327 , 557–560 (2003).

Codebook for the Oxford Covid-19 Government Response Tracker. GitHub https://github.com/OxCGRT/covid-policy-tracker/blob/master/documentation/codebook.md (2020).

Harrer, M., Cuijpers, P., Furukawa, T. & Ebert, D. Doing Meta-analysis in R: a Hands-On Guide (Zenodo, 2019).

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Acknowledgements

The authors thank M. Ryn for assistance with coordinating data collection, and S. Castello for assistance with organization and coordination between collaborators. The research in this paper is financially supported by the Utrecht University Faculty of Social and Behavioural Sciences COVID-19 Fund. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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M.P.E., A.E.N. and M.F.T.P. conceived the project and initiated data collection. A.E.N. and J.V. prepared the data. A.E.N. conducted the analyses and wrote up the results. A.E.N., R.Z., M.P.E. and L.M. interpreted the analyses and wrote the manuscript. M.P.E. coordinated the project. A.A., S.A., B.A., M.J.A.B., R.Ag., R.As., D.B., H.-M.B., J.E.H.B., M.B., G.B., I.A.C.-E., S.C.-H., R.D., C.D., D.F., M.G., K.-H.J., J.K., T.L.-S., W.-S.L., R.L.R., L.M., G.M., A.E.N., N.P., M.F.T.P., R.P.-C., S.R., R.S., N.T., C.J.V.P. and R.Z. contributed data or facilitated access to data used in the analyses. All authors critically reviewed the manuscript and contributed to intellectual content.

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Nivette, A.E., Zahnow, R., Aguilar, R. et al. A global analysis of the impact of COVID-19 stay-at-home restrictions on crime. Nat Hum Behav 5 , 868–877 (2021). https://doi.org/10.1038/s41562-021-01139-z

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How to persuade people to stay home: A century of social science research offers clues on human behavior

With social distancing and shelter-in-place mandates in effect worldwide, the COVID-19 pandemic is necessitating large-scale behavior change and taking a significant psychological toll. How can leaders and the media promote cooperative behavior? What kind of messages work best? Northwestern University professor of political science Dr. Jamie Druckman and University of Cambridge social psychology professor Dr. Sander van der Linden addressed these questions and more in a Northwestern Buffett webinar this week, drawing on a century of social science research that sheds light on how to better align human behavior with public health officials’ recommendations. Here are six key takeaways:

Quite a bit has changed since the Spanish flu pandemic of 1918, but human behavior has not. A paper published in Science magazine in 1919 illuminated the factors that stood in the way of preventing the spread of the Spanish flu of 1918, and these remain critical challenges today, Dr. Druckman said. “People do not appreciate the risks they run,” he said. “It goes against human nature for people to shut themselves up in rigid isolation as a means of protecting others, and people often unconsciously act as a continuing danger to themselves and others.”

“Loose” cultures have seen steeper COVID-19 curves than “tight” ones: Efficient governments and “tight cultures” can help mitigate the risk of people acting against their best interests, Druckman said. Countries with stronger stay-at-home orders and less heterogeneity in terms of their response to COVID-19 have seen their curves flatten faster. Yet what a “tight culture” looks like can vary significantly across the globe. Countries like Sweden that appear laissez faire in their response to COVID-19 have relatively small populations and health care systems that are considered well-equipped to deal with the projected number of COVID-19 cases, Dr. van der Linden noted.

Data also suggests a correlation between strong public understanding of a government’s response to COVID-19 and fewer COVID-19 cases, Druckman said. Germany is one example: “The German public has a stronger understanding of its government’s response to COVID-19, and we see a lower rate of infection and lower death toll there,” he noted.

Unfortunately, the American public doesn’t have as strong an understanding of the U.S. government’s response to COVID-19 and this, coupled with a “looser” culture, has contributed to a steeper rise in COVID-19 cases, Druckman noted. “The U.S. was uniquely bad in terms of the rate at which it surpassed 500 confirmed cases of COVID-19. The government didn’t act quickly enough to flatten the curve,” he said.  In the absence of tightly coordinated U.S. federal government measures, the private sector has stepped in and far outside of its comfort zone: New Balance is producing hypebeast grade masks , General Motors and other auto manufacturers are producing ventilators, and the New England Patriots are flying N95 masks in on their private planes, to name a few examples.

Compliance largely depends on unity and credibility: Persuading the public to comply with stay-at-home orders and other social distancing recommendations depends, in large part, on cohesion: “Bipartisan messages are crucial in the U.S.,” Druckman said. “This is clear from the research not only on COVID-19 but a whole host of other issues. Bipartisan messages are much more persuasive.” It can be difficult to see common ground, however, amid the abundance of headlines claiming wild variation in compliance with public health recommendations among Democrats and Republicans. Druckman urges people to view these headlines with skepticism: It can be easy, yet misleading, to paint a picture of COVID-19 along partisan lines, he said. Counties with a more republican vote have exhibited less social distancing behavior, but many of these counties are in rural areas that require less extreme social distancing measures to begin with.

In terms of specific messages, evidence suggests those that have been most effective in persuading people to adhere to social distancing guidelines are those that “urge people to act for the common good, highlight the story of a specific—and young—victim, and explain the dynamics of virality,” Druckman said, pointing to this example: “On average, each person passes the coronavirus on to two to three people. If you break a chain of transmission, you can single-handedly prevent the suffering of potentially dozens of people.” The source of the message is also important, Druckman added, noting messages from local officials can be more effective, given “you can imagine they’re experiencing exactly what you’re experiencing, and that enhances the credibility of the message.” 

The words we use matter: “Social distancing” needs to be distinguished from physical distancing as we are in the midst of “a perfect storm for a mental health crisis,” an uptick in domestic violence and ethnic scapegoating that makes strong social support networks critical, even if activated at a distance, Druckman said. “There is also an optimal level of fear,” he noted. Inducing too much of it can have a paralyzing effect. “Worry” tends to motivate more productive responses in times of crisis over “fear.” Collective terms, such as “us,” also tend to bring out the best in us, Druckman said. “It builds a shared sense of identity” and encourages people to act for the common good.

Preventing the spread of fake news means becoming more attuned to it: Druckman talked about the importance of separating science from science fiction, pointing to a recently published article recommending people stay at least six feet away from each other when biking, walking, or running outside. The article , published on Medium, has not yet been published in a peer-reviewed journal and, as New York Times reporter Gretchen Reynolds pointed out, “ the study did not look at coronavirus particles specifically or how they are carried in respiratory droplets in real-life conditions. Nor does it prove or even suggest that infection risks rise if you do wind up temporarily strolling behind a panting runner.” Medium posted a disclaimer at the top of this article—“Anyone can publish on Medium per our  Policies , but we don’t fact-check every story. For more info about the coronavirus, see  cdc.gov .” Likewise, YouTube includes a note under COVID-19 related video posts like this one from Osmosis.org , encouraging users to “get the latest information from the CDC about COVID-19,” van der Linden noted. “However this may suggest the video is, in fact, from the CDC,” he added. Our eyes often miss or misinterpret disclaimers like this and “we need to test these things.”

Research suggests a promising path toward inoculation against misinformation:   Drawing from Inoculation Theory , van der Linden and other scholars are developing techniques and interventions designed to strengthen citizens’ immunity to fake news. “Injecting people with weakened doses of fake news can help to build up their mental antibodies and resistance to future misinformation,” van der Linden said. The good news is data shows people gravitating back toward major news networks, such as ABC, NBC, and CBS in the U.S., that tend to present more balanced and credible information compared to what people find in social media “echo chambers,” Druckman added.

Ultimately, there are some simple things we can all do to build up our own immunity to and curb the spread of misinformation, including looking for warnings on questionable content and pausing to confirm questionable content before sharing it. Time is certainly of the essence in times of crisis, Druckman said, but cautioned, “If you hurry too much, you might not be taking the right actions.”

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How the Pandemic Has Transformed the Idea of Home

Lockdowns and restrictions have forced millions of people around the world to adapt to their spaces and exist within the confines of four walls. We asked readers how their relationship to their homes was affected.

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By Sara Aridi

Before the pandemic began, your home may have merely been a place to sleep. Or maybe it was a precious escape from the hustle and bustle of the outside world. But now, a year after the World Health Organization declared the coronavirus outbreak a pandemic , home has taken on an entirely new meaning. A year of lockdowns and various restrictions have forced millions of people around the world to adapt to their spaces and exist within the confines of four walls. Many could no longer afford to pay rent and subsequently lost their homes, their sense of security.

So we asked readers : How has the pandemic affected your relationship to your home? Nearly 300 people wrote in with a range of perspectives. Here are a few of them, edited and condensed for clarity.

I have been “housebound” for several years with an illness, so Covid was a significant threat for me. When I bought my house 10 years ago, it was a dream come true. I never thought I would own a home, so I started a little ritual each morning: I stand with my coffee and look out at the garden. For a minute or two, I think about all of the good things in my life and how lucky I am to have a home. Since Covid, this ritual has even more significance for me. So many people are struggling to keep their homes. Mine has become a fortress against a dangerous, invisible invader. It has kept me safe. — Susan R., Fallbrook, Calif.

Home feels like both a refuge and a cage. We are lucky to have plenty of room inside and outside, but, like water, we seem to fill it all up. — Chris O’Connor, Ossining, N.Y.

My busy life came to a halt in March 2020. I stopped working three jobs and really started spending time with my three children. We cooked and ate more meals together. We went on daily bike rides. We sewed 900 masks together. We became a “family” again. I was always so busy that I forgot how much I love being with my kids. Home is the center of who we are. My family is stronger and closer. — Carrie Youngren, Mount Vernon, Wash.

A Year Without Travel

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• Melbourne School of Design

Why staying home during a pandemic can increase risk for some

By Dr Katrina Raynor, Dr Ilan Wiesel, Professor Bec Bentley Affordable Housing Hallmark Research Initiative

As coronavirus infections began to rise in Australia, citizens were ordered by government to stay home. “If you can stay at home, you must stay at home” has become the motto for good citizenship, and our primary weapon against a cureless pandemic. However, over less than two months, the experience of staying home exposed the inadequacy of housing for many people.

As coronavirus infections began to rise in Australia, citizens were ordered by government to stay home. “If you can stay at home, you must stay at home” has become the motto for good citizenship, and our primary weapon against a cureless pandemic. However, over less than two months, the experience of staying home exposed the inadequacy of housing for many people.

Housing features such as tenure, density and design have become key factors determining people’s ability to stay home, to work or study from home effectively, to isolate from other members of the household if necessary, and more generally to protect themselves and others, especially those who are more vulnerable, from the risk of contracting coronavirus.

Housing tenure, design and quality influence ability to stay home

Security of tenure, capacity to adapt to changing circumstances and affordability all play a role in resident’s ability to shelter safely during COVID19. Even before COVID19, over two-thirds of low-income renters in Australia were in housing stress and over 31% of renters were in leases of 6 months or less. With limited ability to make changes to their homes or ask for repairs tenants also have less capacity to adjust their homes to allow for safe segregation of occupants if necessary. Confronted with high levels of insecurity and low affordability, renters may move to overcrowded homes to share housing costs or find themselves homeless or couch surfing; both movements are associated with higher risks of contagion. Many households may unexpectedly find themselves in this situation due to the economic downturn triggered by COVID. Such conditions are amplified for international students who cannot return home, may have lost casual work and face uncertainty in the short to medium term about their housing.

Homeowners face other restrictions in their ability to stay home. Homeowners have far less flexibility to move if they find their home is inappropriate for safe isolation. There is growing evidence that many households are forming and dissolving in response to COVID19 job losses and shelter-in-place measures. Younger people are moving home to their families, international students have returned to their countries of origin (or didn’t come to Australia in the first place) and many households have relocated as they no longer require proximity to their work places. Such movements are much easier for renters that face lower transaction costs when moving.

The physical design of homes is a critical factor shaping their capacity to provide safe shelter. This may be seen in overcrowding of homes that reduce capacity to isolate from other residents. As clusters of COVID19 linked to overcrowded dormitories in Singapore have shown, internal densities and extended close contact in indoor spaces are key factors in the spread of COVID-19. It is not density itself that is necessarily the problem but the ways homes are designed to manage different densities. Places that promote sustained contact while indoors, like dormitories, rooming houses or hospitals, present far higher risks of contagion. This will also be a consideration in apartment lobbies and lifts.

The impact of buildings also manifests in differing levels of housing quality and amenity – isolating in drafty, poorly-insulated and mouldy homes only serves to exacerbate health concerns. Similarly, households now balancing work, leisure, home-schooling and domestic activities all in one space will know well the fine balancing act of juggling multiple uses in one space. Small homes and those designed without children in mind will be particularly challenging at this time. Confinement, loss of routine and reduced contact causes stress, as well as boredom, frustration, and sense of isolation. As we turn to new or existing hobbies such as baking, gardening, exercising or craft projects, some homes will have space for these activities while many others do not.

Risks and resources are shared between household members staying home together

The pandemic has highlighted the interdependency between members of the household, and inequalities between different types of households. The composition of a household - the profile of occupants, their combined resources and relationships to one another - determines occupants’ capacity to ‘stay safe and stay home’. It is not enough to consider the health risk factors of an individual without reference to their household. For example, all members of a household need to take extra precautions when staying home with an elderly or immuno-compromised occupant.

People living in share houses face a unique profile of challenges. Household members may not hold the same views on compliance with government recommendations. People have reported concern about housemates having visitors or working in the health care sector. Where one member of a share house moves out, there are potential repercussions for remaining members’ rent and their ability to sustain their tenancy.

Working and studying from home has also presented challenges for share households. Simple examples include sharing internet connections during peak time and finding space in the house for each household member to work or study effectively. Further to this, some households report a lack of control over internet plans and infrastructure upgrades that will enable them to work and study online.

All household members share exposure in a pandemic. If one household member is an essential or key worker who needs to continue to travel and be in contact with others in the community, all household members’ risk is higher. This has been demonstrated by the recent outbreak of COVID cases centered around Cedar Meats in Victoria whereby family of employees of the meat processing plant were amongst those infected. It is also why the provision of temporary housing for healthcare workers who need to shelter away from their households was such a wise move.

During the COVID-19 pandemic, the interdependency and shared risk within households has been exposed in its most extreme in congregate residential facilities. Aged care homes, in particular, have emerged as the epicentre of contagion and death in the coronavirus pandemic.

In some of the worse affected countries, such as the United States, Belgium and Sweden, it is estimated that between a third and a half of all COVID-19 deaths occurred in aged care facilities.

In Australia approximately one in three deaths occurred in aged care facilities. The susceptibility of aged care facilities results from a combination of factors including: a high concentration of people with underlying health risk factors; exposure to other residents in common facilities, often including kitchens and bathrooms; and exposure to infection through staff and visitors moving in and out of the facility and in direct physical contact with residents. A similar suite of concerns are relevant for people with disability who require support workers and family to visit their home. People with disabilities have less capacity than other community members to minimise and regulate their exposure, causing concern for themselves, their families and advocates.

Attempts to protect aged-care residents from infection have included strict social distancing measures, including controversial bans on visitors, which left many residents socially isolated and reduced community monitoring of the standard of care provided in these facilities.

216,000 residential aged care places have been provided in Australia in 2018. In addition to these, other forms of congregate facilities – such as supported housing, large residential institutions for people with intellectual disability, and prisons – share similar vulnerabilities.

Housing matters now more than ever

Lockdown restrictions are now easing in Victoria and the ‘stay home’ directive was formally replaced with a ‘stay safe’ message on 1st June. However, until a cure or vaccination is made available en masse, COVID-19 is here to stay, with the possibility of new restrictions reinstated whenever infections rise above a predetermined threshold.

The way we think about health, housing, risk and vulnerability needs to change to reflect the ‘new normal’ of increasingly frequent viral pandemics. Housing and household risk are overlapping concepts and policy responses can’t afford to consider these ideas in silos. Access to secure and affordable housing plays a substantial role in public health and safety during a pandemic, just as considering the composition of households can extend traditional thinking about individual health risk factors.

Housing inequalities have always compounded and reflected inequalities in health, wellbeing, and productivity. The imperative to stay home during COVID-19 has amplified these effects. Alongside individual characteristics such as poor health, low income, age and gender, housing related factors are now significant factors mediating vulnerability to, and the varied experiences of, the COVID-19 pandemic. The way that people are housed matters more than ever and the consequences for people’s health (including their mental health) and economic security are greater than they have been in most of our lifetimes.

Dr Katrina Raynor, Dr Ilan Wiesel, Professor Bec Bentley.

Dr Katrina Raynor , Faculty of Architecture Building and Planning, [email protected]

Dr Ilan Wiesel , School of Geography, [email protected]

Professor Bec Bentley , Melbourne School of Population and Global Health, [email protected]

The Hallmark Research Initiative for Affordable Housing is researching the ways in which housing has cushioned or amplified experiences of vulnerability or resilience during COVID19.

To join the discussion or find out more, contact [email protected] .

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Work From Home During the COVID-19 Outbreak

The COVID-19 pandemic made working from home (WFH) the new way of working. This study investigates the impact that family-work conflict, social isolation, distracting environment, job autonomy, and self-leadership have on employees’ productivity, work engagement, and stress experienced when WFH during the pandemic.

This cross-sectional study analyzed data collected through an online questionnaire completed by 209 employees WFH during the pandemic. The assumptions were tested using hierarchical linear regression.

Employees’ family-work conflict and social isolation were negatively related, while self-leadership and autonomy were positively related, to WFH productivity and WFH engagement. Family-work conflict and social isolation were negatively related to WFH stress, which was not affected by autonomy and self-leadership.

Conclusion:

Individual- and work-related aspects both hinder and facilitate WFH during the COVID-19 outbreak.

The COVID-19 outbreak has made working from home (WFH) the new way of working for millions of employees in the EU and around the world. Due to the pandemic, many workers and employers had to switch, quite suddenly, to remote work for the first time and without any preparation. Early estimates from Eurofound 1 suggested that due to the pandemic, approximately 50% of Europeans worked from home (at least partially) as compared with 12% prior to the emergency. Currently, these numbers are approximately the same, with many employees and organizations possibly opting for WFH even after the pandemic. 2

To contain the spread of the virus, Italy quickly adopted home confinement measures which, since the Spring of 2020, were renewed for several months and are still, as in some other European countries, ongoing also during Spring 2021.

As all organizational changes, WFH too has some advantages and disadvantages. 3 Usually, adopting this flexible way of working has been presented as a planned choice that requires a period of design, preparation, and adaptation to allow organizations to effectively support employees’ productivity and ensure them better work-life balance. 4 – 6 However, the COVID-19 outbreak has substantially forced most organizations to adopt this way of working, often without providing employees with the necessary skills required for remote work. 7 – 9 As previously mentioned, studies have reported both advantages and disadvantages related to remote work. 10 Its effects, therefore, have been quite explored. 6 On the other side, the need to examine how WFH, as a “new way of working,” 11 , 12 has affected the well-being and productivity of employees with no prior remote work experience and to identify specific work conditions affecting remote work during the COVID-19 crisis 9 is imperative.

To achieve these goals, the present study considered the Job Demands-Resources (JD-R) model 13 , 14 as a theoretical framework. The JD-R model is a well-established theoretical model in the field of occupational health psychology, which suggests that work conditions, categorized into job demands and job resources, affect employees’ wellbeing and performance. Job demands refer to the physical, psychological, or socio-organizational aspects of the work whose energy-depleting process induces people to experience energy loss and fatigue, leading to stress, burnout, and health impairment. On the contrary, job resources refer to the physical, psychological, social, or organizational aspects of the job that reduce job demands while stimulating work motivation, personal growth, and development. 15 In addition, personal resources have been introduced in the JD-R model defining them as “aspects of the self that are generally linked to resilience and refer to individuals’ sense of their ability to control and impact upon their environment successfully,” 16 thus stimulating optimal functioning and lessening stress.

According to the JD-R model, every occupation and work has its own specific job demands and job resources; hence, the present study considered some job demands, one job resource, and one personal resource to investigate how much they affect employees’ work engagement, job-related stress, and job performance.

The model we developed for this study considered some characteristics of remote work as job demands: the difficulty of adequately reconciling private and work commitments, 17 the decrease or lack of the social context that employees normally experience in the workplace and that is related to the perception of being more socially isolated, 18 and the difficulty of arranging a suitable workstation at home for carrying out their work activities. 19 One of the most prominent job resources when WFH is job autonomy. 5 , 20 – 22 Finally, we considered self-leadership, defined as a self-influence process to behave and perform by setting one's own goals and monitoring their fulfilment, 23 as a personal resource that may potentially contribute to efficient remote work.

The present study integrates research on remote work during the COVID-19 pandemic 8 highlighting some job demands and resources that may affect negative (work stress) and positive (work engagement and job productivity) outcomes of employees’ remote work. Furthermore, since the trend toward remote work is expected to increase even after the pandemic, this study may provide useful information on the individual- and work-related consequences of remote work during and after the pandemic.

Analyzing more in detail the above-mentioned variables, the difficulty of reconciling private and work commitments is often described in the literature as family-work conflict, which is a condition when employees’ participation in work duties is complicated by the involvement of family-related activities. 24 Family-work conflict is usually considered a sex-dependent phenomenon 25 because, in most cultures, the primary responsibility for caregiving and housework tasks 26 lies with women, who are more penalized than men in times of crisis. 27 However, COVID-19 has forced millions of people to stay home, breaking down the distinction between private and work life regardless of age or sex. Therefore, we argue that family-work conflict can be an issue that may potentially affect not only women but men alike when WFH. On the contrary, previous studies hypothesized that remote work can simultaneously reduce family-work conflict as well as amplify it, 4 , 6 nullifying the benefits of WFH. 28 Besides, the confinement that was imposed in the early period of the pandemic may also accentuate this conflict, with family commitments interfering with work commitments.

At home, the presence of partners and children (especially if still in their childhood) engaged in work and school activities, the disruption of child-care and education services observed during the pandemic, and having to contribute towards household chores greatly affected remote workers. 19 For example, employees have to regularly prepare meals three times a day (breakfast, lunch, and dinner) for the whole family, assisting children to connect with their online distance teaching in the morning, assisting with their homework in the afternoon, and spending some quality time with them when their homework is completed. As a result, employees have to work with greater family-work conflict, which we believe negatively affects their job productivity and work engagement while impacting on stress related to the remote work pending completion, in line with the previous literature. 4 , 5

Workplace isolation is another important key feature of WFH during the pandemic. 18 Although previous research highlighted that social isolation is one of the main drawbacks of remote work, 29 – 32 its incidence has inevitably increased during this period. The pandemic has exposed people to social confinement and thus higher levels of loneliness, 8 , 33 which may correlate with declining work satisfaction and performance as well as stress enhancement. 4 , 18

Prior to COVID-19, studies found a negative correlation between time spent telecommuting and individual and team performance. 9 Furthermore, the amount of time spent teleworking and the extent of face-to-face interaction were found to moderate, respectively negatively and positively, the relationship between professional isolation and job performance. 34 In line with previous research, 35 the use of digital technologies to communicate may only partially mitigate the isolation experienced by workers in comparison to the social contacts that are usually experienced by individuals in their workplaces as well as in social life, such as attending the gym or meeting friends. Therefore, as the social confinement observed in this study was extended for many weeks, with no in-presence contact with colleagues, we believe that social isolation is a relevant job demand related to WFH in times of COVID-19. Drawing on this statement, we argue that social isolation is significantly and negatively associated with WFH outcomes concerning job productivity and engagement and positively associated with WFH stress-related levels.

Another peculiarity of WFH during the pandemic is that employees have to share their workspace with family members, such as the partner and/or school-age children engaged in distance-learning primarily. Therefore, it should be noted that WFH during the pandemic has brought about many difficulties in the Italian population as well as in other European countries where social confinement has been adopted for several weeks. The houses were often unsuitable to host more people engaged in study and work activities, 19 thus generating a distracting environment. A previous study conducted on teleworkers 36 highlighted that control on the work environment is positively related to job satisfaction, whereas distractions while working generates work environment dissatisfaction. Studies suggested that a positive full-time WFH experience is associated with the quality of the workspace, such as control on light and acoustic isolation 37 and a workspace that is sufficiently separated from the living space. 38 When this separation is not possible, working in a space with environmental distractions may represent an additional and relevant job demand. Specifically, we hypothesized that environmental distractions are negatively associated with productivity and engagement in remote work and positively with stress.

According to the JD-R model, 13 , 14 job and personal resources affect employees’ well-being and productivity. One of the most prominent job resources of remote work is job autonomy, 13 , 14 which is the extent of independence and discretion permitted while performing professional tasks. 39 Job autonomy positively associates with the number of hours performed remotely. Furthermore, it positively influences remote workers’ engagement, satisfaction, and performance but negatively affects their stress. 5 , 20 – 22 Job autonomy is a major job resource for employees and, in the right doses, it encourages profitable innovations at work. 40 We argue that the positive effects of job autonomy can be observed or even accentuated during the enforced WFH due to the pandemic. WFH was an unforeseen phenomenon necessitated by the outbreak of the pandemic, and many employees had to cope with this new situation and coordinate with colleagues and supervisors to manage the unprecedented autonomy associated with the remote work. For this reason and in line with the literature, 5 , 10 we posit that autonomy positively associates with productivity and engagement but negatively with stress experienced when WFH during the pandemic.

Finally, in our model, we included one personal resource that is particularly helpful in times of change as it enables employees to actively shape their own job practices and work environment. 41 Unfortunately, there is limited evidence on the effects of personal resources when WFH. Nonetheless, especially in unprecedented times such as this, it is important to investigate the role of work-related personal resources because, differently from personal traits (eg, personality), they can be trained. 11 In the present study, we considered self-leadership, measured in its facets of goal setting and self-observation because among the most salient aspects of WFH, as well as one the mutual consequence of the other (the observation of one's work helps to establish goals, and their achievement must in turn be monitored), as an important resource when WFH. Through self-leadership, individuals regulate and control their behavior, influencing and leading themselves using specific sets of behavioral and cognitive strategies. 42 Self-leading individuals efficaciously monitor their actual performance and the standard they set for themselves thus regulating their own motivation. In this vein, it has been evidenced how self-leadership behaviors facilitate higher psychological functioning, which in turn influences work engagement. 43 , 44 A recent study yielded promising results showing that goal-setting behaviors, a component of self-leadership behaviors, may sustain job satisfaction especially when WFH. 21 In light of this, the present study aims to extend the literature by considering self-leadership and evaluating its relationship with WFH engagement and productivity, as well as with stress levels. According to the JD-R model, 13 , 14 we assumed self-leadership as a personal resource for WFH that positively affects employees’ productivity and work engagement, and negatively affects stress.

Participants and Procedure

A study on the work-life quality of remote workers during COVID-19 was conducted using a self-report questionnaire administered online from May to July 2020, using the Qualtrics platform. At the time of data collection, all the participants were WFH full-time in Italian public and private organizations. Participation in the research was voluntary, anonymous, and without any reward. Prior to filling the questionnaire, the respondents signed informed consent. The research was conducted in accordance with the Declaration of Helsinki and all ethical guidelines on social research were followed. The study was approved by the Bioethics Committee of the University of Bologna.

The study included 209 employees (71.3% women and 28.7% men). The average age of the participants was 49.81 years (standard deviation 9.4, minimum 25, maximum 65). Approximately 70% of the respondents reported having at least one child, and 32% of them reported having children younger than 14 years old. Only 9.1% of the employees in the sample reported being involved in WFH prior to the COVID-19 emergency, suggesting that 91.9% of them were WFH for the first time.

The job demands related to WFH were measured using three different scales. The first scale was measuring family-work conflict, which consists of three items from the scale developed by Netemeyer et al, 45 describing the interference that family life has on work when WFH (eg, “Family stress interferes with my ability to perform work-related tasks”). Perceived social isolation was assessed using four items of the scale by Golden et al, 34 which measures a sense of isolation and lack of support experienced by workers (eg, “I miss face-to-face contact with colleagues”). Finally, the scale of the distracting working environment consists of three items developed by Lee and Brand, 36 which measures the level of distraction experienced during WFH (eg, “In my working area, I experience acoustic distractions”).

Job autonomy was assessed using four items developed by Morgeson and Humphrey. 39 These items measured both the possibilities of autonomy in scheduling work activities and taking work-related autonomous decisions (eg, “My job allows me to make my own decisions about how to schedule my work”).

Self-leadership was assessed using four items of the Revised Self-Leadership Questionnaire 23 measuring both the employees’ behaviors of setting job-related goals and self-observation of their work (eg, “When I work, I always keep my tasks in mind”).

Perceived WFH productivity was measured in a section of the questionnaire requiring to compare the current situation of WFH with that one of the traditional office, experienced in the past, through a single item, already used in a remote work context, 18 whose formulation is “When I work remotely, I am more productive.”

WFH engagement was measured using the three-item version of the Utrecht Work Engagement Scale 46 adapted to the WFH context (eg, “When I work from home, I feel full of energy”).

Finally, stress experienced during WFH was measured through the four items previously adopted by Weinert et al 47 aimed to measure workers’ perception of exhaustion and fatigue due to WFH (eg, “I feel exhausted from working from home”).

All the above measures were evaluated using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). Socio-demographic variables, such as age, sex, caring responsibility, and remote work experience were considered as control variables, as literature recognizes them as possible confounders in the relationships under study. 5 , 8 , 48 Specifically, sex (0 = M; 1 = F), caring responsibility (0 = not having children younger than 14 years old; 1 = having children older than 14 years old), and WFH experience (0 = no; 1 = yes) were coded as dummy variables. Furthermore, since this research was conducted during the first phase of the COVID-19 pandemic, subjective perceptions and concerns regarding the COVID-19 may also have an impact on the experiences and well-being of the participants. 18 Therefore, fear of COVID-19 was used as a control variable and assessed using the seven items of the Italian version of the Fear of Covid-19 Scale. 49 For example, “I am very afraid of COVID-19.”

Data Analysis

Prior to data analysis, the validity and reliability of the scales were evaluated. In particular, the technique of confirmatory factor analysis (CFA) was used to assess the dimensionality of the scales. The reliability and convergent validity of the measures were then evaluated by computing composite reliability (CR) and average variance extracted (AVE) values, while the discriminant validity was assessed by calculating maximum shared variance (MSV) values.

Once established that all the measures in this study had reliability and validity values following the cut-offs usually adopted in research, 50 descriptive statistics and correlations among the major study variables were calculated. Finally, hierarchical multiple regressions evaluated which job demands and job resources influenced the three dependent variables of our study. In the three separated hierarchical regressions performed, one for each dependent variable, the stepwise method was used. In the first step, we included the control variables of sex, age, presence of children younger than 14 years old, WFH condition, and fear of COVID-19. In the second step, we added job demands, namely family-work conflict, social isolation, and distracting work environment. Finally, in the third step, we included the resources of job autonomy and self-leadership behaviors. All the data were analyzed using IBM AMOS and SPSS statistics version 26 (Armonk, NJ).

Test of Measurement Model

First, two CFAs were conducted to compare an eight-factor model, one for each construct of this study, with a model in which all the items were grouped into a single dimension. The eight-factor model showed a greater fit to data ( χ 2  = 503.54; df = 272; χ 2 /df = 1.85; comparative fit index [CFI] = 0.93; incremental fit index [IFI] = 0.93; root mean square error of approximation [RMSEA] = 0.06; and standardized root mean square residual [SRMR] = 0.06) compared with the model with a single factor grouping all the items ( χ 2  = 2147.77; df = 299; χ 2 /df = 7.18; CFI = 0.46; IFI = 0.46; RMSEA = 0.17; and SRMR = 0.16). The fit values of the eight-factor model were good, and each item loaded into its factor with saturation values greater than 0.40. With the only exception of job productivity, measured through a single item, we then calculated the values of composite reliability (CR), average variance extracted (AVE), and maximum shared variance (MSV) for each scale. CR values for each dimension were greater than 0.70, giving evidence of the reliability of the scales. All the AVE values were above the cut-off of 0.50, while each MSV was lower than AVEs, indicating that the study measures had both convergent and discriminant validity. Table ​ Table1 1 reports the results of these analyses.

Values of Reliability (CR) and Convergent (AVE) and Discriminant (MSV) Validity

AVE, average variance extracted; CR, composite reliability; MSV, maximum shared variance.

Descriptive Statistics and Correlations

Descriptive statistics, Cronbach α, and correlations among variables are shown in Table ​ Table2. 2 . All the variables correlated in the expected direction. Job demands were found to be negatively associated with WFH job productivity and work engagement and positively related to WFH stress. The resources of job autonomy and self-leadership were positively related to work productivity and work engagement, but their relationships with stress, although negative, were not significant. Moreover, the two resources were not related to the three job demands.

Descriptive Statistics, Cronbach α, and Correlations Among the Variables

Cronbach α between brackets.

Regression Analysis for WFH Employees’ Productivity, Work Engagement, and Stress

Table ​ Table3 3 shows the results of the multiple regression analyses. Following the steps described in the Method paragraph, the first regression tested WFH productivity as dependent variable. For what concerns control variables, although in step 2 the experience with WFH resulted to be significant, its influence in step 3 revealed to be no more significant while, at step 3, age ( β  = –0.14; P  < 0.05) and fear of COVID-19 ( β  = 0.25; P  < 0.01) resulted, respectively, to affect negatively and positively WFH productivity. In step 2, when job demands were entered, a significant increase in explained variance (ADjR 2  = 0.27; ΔR 2  = 0.24; P  < 0.01), over and above the variance explained by control variables, was observed. At this step, both family-work conflict ( β  = –0.29; P  < 0.01) and social isolation ( β  = –0.29; P  < 0.01), were significantly and negatively associated to WFH productivity, whereas distracting work environment was not significantly associated to it ( β  = –0.05; P  > 0.05). In step 3, job autonomy and self-leadership showed a significant improvement in explained variance (ADjR 2  = 0.32; Δ R 2  = 0.05; P  < 0.01). At this step, both the job demands of family-work conflict ( β  = –0.29; P  < 0.01) and social isolation ( β  = –0.29; P  < 0.01), were negatively related to WFH productivity, whereas both job autonomy ( β  = 0.14; P  < 0.05) and self-leadership ( β  = 0.17; P  < 0.01) were positively related to WFH productivity. Distracting work environment was not significantly associated with it ( β  = –0.02; P  > 0.05).

Regression Parameters: Standardized Coefficients and Overall Changes in R 2 for WFH Job Productivity, Work Engagement, and Stress

Method: enter.

Gender: 0 = M, 1 = F; WFH experience: 0 = no, 1 = yes; Children <14: 0 = no, 1 = yes.

The second regression tested remote work engagement as a dependent variable. About the control variables, also in this case, in step 2, the experience with WFH resulted to be significant, while in step 3 its impact was no longer significant. Furthermore, in this case, fear of COVID-19 ( β  = 0.19; P  < 0.01) positively and significantly affected remote work engagement even after inserting variables at steps 2 and 3.

In step 2, when entering job demands, a significant increase in variance was observed (ADjR 2  = 0.37; ΔR 2  = 0.31; P  < 0.01), over and above the variance explained by control variables in the first step. Specifically, step 2 of this regression shows that all the three job demands of family-work conflict ( β  = –0.19; P  < 0.01), social isolation ( β  = –0.36; P  < 0.01) and distracting work environment ( β  = –0.18; P  < 0.05) negatively affected work engagement. At step 3, both the resources of autonomy ( β  = 0.19; P  < 0.01) and self-leadership ( β  = 0.23; P  < 0.01) positively affected work engagement. All the three job demands of family-work conflict ( β  = –0.19; P  < 0.01), social isolation ( β  = –0.36; P  < 0.01) and distracting work environment ( β  = –0.14; P  < 0.05) were still negatively associated to work engagement, and an increase in the explained variance (ADjR 2  = 0.44; ΔR 2  = 0.10; P  < 0.01) was observed.

Finally, our focus shifted to the impact of WFH on workers’ well-being. Using WFH stress as a dependent variable, the third hierarchical regression did not show any effect of the control variables on this outcome. At step 2, both family-work conflict ( β  = 0.31; P  < 0.01) and social isolation ( β  = 0.48; P  < 0.01), but not distracting work environment ( β  = 0.05; P  > 0.05), were positively related to stress showing a significant increase in explained variance (ADjR 2  = 0.44; ΔR 2  = 0.42; P  < 0.01). At step 3, both family-work conflict ( β  = 0.31; P  < 0.01) and social isolation ( β  = 0.48; P  < 0.01), but not distracting work environment ( β  = 0.05; P  > 0.05), were positively associated with stress. On the contrary, neither autonomy nor self-leadership had a significant impact on WFH stress. Therefore, no significant increase in explained variance was observed (ADjR 2  = 0.44; ΔR 2  = 0.00; P  > 0.05).

The present study examined employees’ well-being and productivity when WFH during the pandemic. We addressed this issue by using the JD-R model 13 , 14 as a framework and by investigating the effect that specific WFH job demands and resources have on WFH outcomes. Among the job demands, we examined the effects of family-work conflict, social isolation, and distracting environment. Job autonomy was evaluated as a job resource, and self-leadership as a personal resource. The JD-R model 13 , 14 has also practical implications since it not only allows to focus on job-related risk prevention strategies (by decreasing job demands) but also on benefit promotion (by increasing job resources and, when possible, personal resources) to sustain employees’ productivity and work engagement and decrease the stress experienced when WFH for the long periods required from the pandemic. In a time in which employees had to adapt quickly to WFH, the identification of obstacles, as well as of enablers, to well-being and job performance is a priority for many organizations, and this study contributes to this purpose. Overall, findings observed in the present study are in line with the assumptions developed following the theoretical framework of the JD-R model 13 , 14 and also consistent with the literature related to remote work.

Social isolation and family-work conflict were associated with all the three tested outcomes, in the direction we envisioned, thus proving to be important job demands of remote work that can significantly decrease productivity and work engagement on the one hand and increase job stress on the other. These results are in line with previous studies 4 , 8 , 18 and also improve extant knowledge concerning the relationship with productivity, engagement, and stress experienced during WFH. Findings suggest that organizations and employees should consider these factors and develop guidelines on how to better manage them to observe the positive outcomes typically expected from remote work. In particular, increasing opportunities to communicate with colleagues and superiors represents the first strategy for organizations, HR officers, and employees, because communications can decrease social isolation perceptions. The available technological resources can do a lot in this direction: although lean communications, such as e-mails, allow an exchange of information often functional for work, the social exchange between human beings takes place through “richer” forms of interactions, among which the face-to-face interaction represents the “gold standard.” 51 Many companies accelerated the acquisition and use of technologies and software that offer interactive experiences that imitate the face-to-face or group interactions among people. The other side of the coin, however, concerns the issue of the digital privacy defense and the fear of digital surveillance 52 , 53 that the massive use of technologies may increase. At the same time, managers and HR officers should also effectively reflect on the frequency, timing, and structure of such communicative exchanges to avoid the risk of excessive interruptions and distractions of workers.

The theme of distractions is, in fact, another major issue related to WFH. The results of this study capture the deleterious role that family-work conflict and a chaotic environment, characterized by visual and acoustic distractions and lack of privacy, play on WFH outcomes. Distracting environments, while fortunately proving not to be predictors of reduced productivity and increased stress, seem to exert a negative influence on the motivational drivers of people. Employees may decrease their engagement, with weakened work motivation when their work setting becomes more distracting. The family-work conflict, instead, has shown significant and unfavorable effects on every dependent variable of this study. Probably, its centrality—already known in research on telework 4 —is also increased by the contingent situation related to the COVID-19 pandemic: in this period, workers’ homes are often “crowded” by cohabitants grappling with their work and educational commitments. A crowded home further complicates the family and work-life balance, a learning process that previous studies suggested to require 1 year of WFH experience. 5

Learning how to manage remote work can decrease the perception of family-work conflict. In addition, organizations should support employees’ time management skills, enabling them to divide the two spheres and give each of them the right attention at the right time, with a view to the right to disconnection and physical and mental recovery of each worker.

The importance of personal work management skills is also underlined by the resources tested in this study. Our findings show that autonomy and self-leadership have a positive relationship with productivity and work engagement. So, they may represent two relevant resources, able to sustain WFH productivity and engagement during the COVID-19 pandemic, and to potentially bring favorable outcomes for both organizations and employees. In practical terms, promoting autonomy and self-leadership may be a solution to improve the efficacy of remote work programs and related implications in terms of WFH engagement. In light of this, training interventions may be supplied to WFH employees to develop self-observation strategies and to promote the schedule of work-related goal-based deadlines and priorities. Furthermore, these findings call attention to new work processes supporting the work autonomy of individuals, leveraging the specific skills of individuals, and providing functional tools for job management in the new context of remote work. Advancements in this sense seem fully compatible with work visions that are increasingly geared to working towards objectives and less based on directive leadership processes, and instead more participatory. 54 Consequently, organizations should empower workers through training courses aimed at developing self-leadership behaviors.

No significant relationship has been observed between resources and stress levels. In the JD-R model, job and personal resources are expected to directly impact well-being and motivational processes or to moderate the impact of job demands on stress and ill-health. 16 These results suggest that future studies should investigate the buffering role of specific WFH jobs and personal resources on the relationship between WFH demands and stress.

Other notable findings should be outlined. Our results suggest that remote work can be a useful solution especially for people concerned about COVID-19. In line with the previous literature, 18 the perceptions of people about the COVID-19 virus seem to play an important role in work during the pandemic. Our findings show that the fear for this pathogen is positively associated with higher levels of productivity and engagement. In other words, people emotionally affected by COVID-19 also reported being more productive and motivated when WFH. This suggests that, consistently with the literature, 55 , 56 this way of working may also play a protective, anxiety-relieving role for workers, since they were not asked to go to work, and thus be exposed to possible contagion by leaving home. On the other hand, we also observe that perception of lower productivity is associated with the increasing age of workers, a result probably explained both through the difficulties that these employees may have with technological tools, and their potential less ability to adapt to changes, 57 especially if they take place quickly.

There are some implications for future research in this field that derive from the present study. Indeed, our model, although including many variables, gives only a small account of the many dynamics that underlie the complex phenomenon of the WFH. Based on this, we believe it is important that future studies take into consideration, with a more specific research design and a more representative sample, other constructs, particularly among the job and personal resources. In particular, we point out that the PsyCap, a psychological state consisting of the dimensions of self-efficacy, optimism, resilience, determination, 58 applied both at the personal and team level, can open important horizons for future studies, which still have much to investigate on the complex reality of remote work and its outcomes in terms of employees’ well-being and health.

We also point out some of the limitations of this study, as well as some suggestions for future studies. One limitation of this study is its cross-sectional design, which allows us to trace associations between the investigated constructs but on the other hand does not allow determining causal relationships between the variables. Furthermore, we also believe that to generalize the results may be not possible, since our sample was a convenience sample, susceptible to biases, including the fact that the data collection took place online, among people accustomed to the use of digital technologies.

In this study, we investigated if WFH-related job demands and job resources are related to remote work productivity and work engagement as well as on stress. We found that the empirical results we analyzed and discussed, except for the relationships between distracting working environment and the outcomes of productivity and stress, and the relationships between both autonomy and self-leadership and stress, mostly confirmed our assumptions.

We believe that this study contributes to the literature concerning remote work and the well-being of remote workers that, during the COVID-19 pandemic, which is marked with relevant emotional and health implications. Furthermore, the implications of this study are of further importance as they provide information concerning the needs of workers who have had to adapt to enforce full-time WFH due to the pandemic, most of whom have no prior WFH experience. Managers, HR officers, and workers engaged in remote activities should consider family-work conflict, social isolation, and distracting work environments as potential obstacles and job autonomy and self-leadership as potential enablers of WFH engagement. In times of pandemic, such as the COVID-19, where containing the spread of the disease is crucial, WFH is a key opportunity and can give a competitive advantage to sustain and improve performance of organizations.

Funding Sources: nothing to declare.

Conflict of Interest: nothing to declare.

Ethical Considerations & Disclosure: This research fully respects the Declaration of Helsinki. All ethical guidelines were followed. The Bioethics Committee of the University of Bologna formally approved this study.

Clinical significance: The COVID-19 pandemic has caused people to work from home, in most cases without any preparation. Our results help identify factors associated with employee well-being, charting ways to follow to make sure people at home can work without negatively impacting their health.

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The Impact of COVID-19 Stay-At-Home Orders on Health Behaviors in Adults

Affiliation.

• 1 Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA.
• PMID: 33043562
• PMCID: PMC7675243
• DOI: 10.1002/oby.23066

Objective: Stay-at-home orders in response to the coronavirus disease 2019 (COVID-19) pandemic have forced abrupt changes to daily routines. This study assessed lifestyle changes across different BMI classifications in response to the global pandemic.

Methods: The online survey targeting adults was distributed in April 2020 and collected information on dietary behaviors, physical activity, and mental health. All questions were presented as "before" and "since" the COVID-19 pandemic.

Results: In total, 7,753 participants were included; 32.2% of the sample were individuals with normal weight, 32.1% had overweight, and 34.0% had obesity. During the pandemic, overall scores for healthy eating increased (P < 0.001), owing to less eating out and increased cooking (P < 0.001). Sedentary leisure behaviors increased, while time spent in physical activity (absolute time and intensity adjusted) declined (P < 0.001). Anxiety scores increased 8.78 ± 0.21 during the pandemic, and the magnitude of increase was significantly greater in people with obesity (P ≤ 0.01). Weight gain was reported in 27.5% of the total sample compared with 33.4% in participants with obesity.

Conclusions: The COVID-19 pandemic has produced significant health effects, well beyond the virus itself. Government mandates together with fear of contracting the virus have significantly impacted lifestyle behaviors alongside declines in mental health. These deleterious impacts have disproportionally affected individuals with obesity.

© 2020 The Obesity Society.

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Conflict of interest statement

The authors declared no conflict of interest.

CONSORT diagram of the survey…

CONSORT diagram of the survey responses.

Overall changes to ( A…

Overall changes to ( A ) dietary behaviors (REAP‐s), ( B ) time…

Changes in individual contributors to…

Changes in individual contributors to the overall REAP‐s score. A positive change is…

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• Published: 23 November 2020

“Staying at home” to tackle COVID-19 pandemic: rhetoric or reality? Cross-cutting analysis of nine population groups vulnerable to homelessness in Japan

• Masami Fujita 1 ,
• Sadatoshi Matsuoka 1 ,
• Hiroyuki Kiyohara   ORCID: orcid.org/0000-0002-3721-6935 1 ,
• Yousuke Kumakura 2 ,
• Yuko Takeda 3 ,
• Norimichi Goishi 4 ,
• Masayoshi Tarui 5 ,
• Masaki Inaba 6 ,
• Mari Nagai 1 ,
• Masahiko Hachiya 1 &
• Noriko Fujita 1  

Tropical Medicine and Health volume  48 , Article number:  92 ( 2020 ) Cite this article

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Although the “stay-at-home” order is advocated against the coronavirus disease 2019 (COVID-19), the lives of individuals lacking adequate housing are threatened. We developed a framework to assess various populations with unstable housing in terms of socio-economic consequences of COVID-19, risk of COVID-19 infection and progression, existing/urgent measures, and remaining challenges. Within the framework, nine groups vulnerable to homelessness in Japan were classified into (i) “people without accommodation,” (ii) “people living in temporary or crisis accommodation,” and (iii) populations that include “people living in severely inadequate and insecure accommodation.” The assessment revealed that “staying at home” was physically and practically unattainable across groups. The study identified specific institutional, social, and cultural challenges apart from the common economic crisis, whereas the utilization of social welfare was low. Findings suggest that the rapid increase of groups classified as “(i)” and “(ii)” should be addressed by engaging stakeholders to enhance the availability and accessibility of social welfare and rescue measures, and to ensure safe and private accommodations for all groups. It is critical to enhance multi-sectoral collaboration in responding to the common and specific vulnerabilities of these population groups from health, socio-economic, and humanitarian perspectives. Under the pandemic, homelessness should be regarded less as a peculiar problem for specific populations but an extension of daily life. The framework can be a reference when planning the comprehensive yet concise assessment of populations with unstable housing in other countries to inform responses to the pandemic.

Introduction

Coronavirus disease 2019 (COVID-19) affected people at the global scale and brought a devastating impact on disadvantaged populations [ 1 ]. Assessing members of society likely to be affected by COVID-19 is essential to effectively respond to the pandemic in each country as entities classified under disadvantaged groups can change [ 2 ]. Especially, homeless individuals are affected by the COVID-19 pandemic [ 3 ].

Homeless people, who are reported to carry five- to tenfold mortality rates compared with other groups, are engaged in situations prone to COVID-19 infection [ 4 , 5 ]. These individuals are at high risk of progressing to severe conditions due to underlying chronic diseases [ 6 ]. However, measures for preventing COVID-19 transmission, such as staying at home, rigorous hygiene, and strict social distancing, are reportedly unrealistic for the homeless [ 7 ].

From the viewpoint of Sustainable Development Goals (SDGs), homelessness is related to several issues, such as poverty, hunger, poor health, lack of access to education, clean water and sanitation, gender, inequalities, and barriers to achieving sustainable and inclusive cities [ 8 ]. Addressing homelessness during the pandemic requires a broad understanding of country-specific situations so that effective multi-stakeholder cooperation and solidarity can be explored through the lens of SDGs and their interlinked nature.

The definition of homelessness varies, which tends to be conceptualized not only by accommodation but also by broad perspectives, such as security of tenure, physical environment of dwelling, and social interaction, as proposed by the Institute of Global Homelessness (IGH) [ 9 ]. The IGH classified the homeless into “people without accommodation,” “people living in temporary or crisis accommodation,” and “people living in severely inadequate and/or insecure accommodation.” In Japan, the definition of homelessness is relatively narrow [ 10 , 11 ] and people without accommodation are frequently highlighted. Many people living in temporary, inadequate, or insecure accommodation tend to be invisible to society [ 12 , 13 ]. Groups with diverse characteristics, such as non-regular employment; lesbian, gay, bisexual, transgender, and queer or questioning (LGBTQ); youth; foreign-origin; and internal displacement, were reported among the latter populations, and COVID-19 may force such groups to face common and specific challenges. However, the condition of these populations remains undocumented or lacks comparative analysis.

Since we could not find any existing tool to conduct such analysis, we drafted a framework that consisted of two dimensions, namely (i) three categories of populations with unstable housing conditions based on the IGH classification, and (ii) COVID-19 related situation and response concerning respective populations, encompassing health, socio-economic, and humanitarian elements. Consideration was given to comprehensiveness and conciseness for developing the framework so that stakeholders of different populations could have a good overview for communication and collaboration across sectors.

Regarding the first dimension, we adjusted the third population of the IGH classification from “people living in severely inadequate and/or insecure accommodation” to populations that include “people living in severely inadequate and/or insecure accommodation.” This adjustment was made to include people who do not necessarily reside in “severely inadequate and/or insecure accommodation,” but are susceptible to losing accommodation, considering the potential devastating impact of COVID-19 pandemic on housing of a broad range of populations [ 14 ].

The second dimension involved characteristics of people including their vulnerabilities, socio-economic consequences of COVID-19, risk of COVID-19 infection and progression, existing and urgent measures taken, and remaining challenges, in line with the COVID-19 operational response strategies for the United Nations (UN) families and partners [ 15 ]. These strategies included COVID-19 Strategic Preparedness and Response Plan (SPRP) for the health sector [ 16 ], UN Socio-Economic Response Framework [ 17 ], and Global Humanitarian Response Plan (GHRP) [ 18 ]. Each of these strategies and related documents highlighted the importance of vulnerable populations including homeless people.

We presented the draft framework to experts and practitioners engaged in unstable housing conditions, including those with expertise and experiences in community health, mental health, and social determinants of health, as well as housing support, livelihood support, and social welfare. They provided suggestions and advice to refine categorization of populations and identify nine socially distinguishable groups of individuals, based on their experiences in working with NGOs, public sector, and academia.

We then applied the framework to assess populations facing difficulties in securing accommodation in Japan with a focus on Tokyo, who were reported to have a wide range of background and characteristics [ 19 , 20 , 21 ]. These nine groups were classified into the abovementioned three categories:

“People without accommodation” refers to individuals living on streets or open spaces.

“People living in temporary or crisis accommodation” comprises three groups, namely (i) free- or low-cost accommodations and public assistance facilities, (ii) self-reliance support centers, and (iii) cyber-homeless.

Populations that include “people living in severely inadequate and/or insecure accommodation” encompass the remaining five groups, namely (i) non-regular workers and self-employed individuals including female-parent household in industries susceptible to repercussions from the COVID-19 pandemic; (ii) impoverished LGBTQ; (iii) evacuees from Fukushima after the nuclear disaster (internally displaced people in Japan); (iv) migrants—technical interns, foreign students, poverty-stricken long-term residents, and undocumented foreigners; and (v) adolescents and children abused in domestic settings.

For each category, a literature review was conducted in terms of characteristics and vulnerabilities, socio-economic consequences of COVID-19, risk of COVID-19 infection and progression, existing measures most relevant to the vulnerabilities, and urgent measures to address the socio-economic consequences of COVID-19 and risk of COVID-19 infection and progression. The literature included journal articles, reports, government documents, and news articles as of May 31, 2020 (The first COVID-19 case was reported on 16 January 2020 in Japan. The number of reported cases increased to 16,851 by 31 May 2020. The Japanese Government issued an emergency declaration on 7 April 2020, and it was extended to 25 May 2020). Tables were constructed and contents refined after consultation with experts for comparative analysis. The following sections present an overview of social security schemes in Japan as background information of the review, main findings of the review, and strategic directions to address challenges.

Overview of social security schemes in Japan

Main social security schemes in Japan include public pension systems, health care systems, public assistance, labor insurance, social welfare for the elderly, family policies, and policies for persons with disabilities [ 22 ]. Income security for the elderly, disabled person, and survivors is provided by the public pension systems. Health care systems include public health, health insurance, and maternal and child health services. Public assistance is offered as part of the financial support system for the poor. Employment insurance, work-related accident insurance, and others are provided as part of the worker protection system. Social welfare for the elderly includes long-term care insurance. Family policies involve childcare and financial support such as child allowance and support for single-parent households. Policies for persons with disabilities include care service provision and financial assistance.

People without accommodation and people living in temporary or crisis accommodation (Table 1 )

Characteristics and vulnerabilities of populations.

People without accommodation are defined as those living their daily lives in city parks, riverside, streets, stations, and other facilities according to the Act on Special Measures concerning Assistance in Self-Support of Homeless. They are predominantly male and aged over 60 years [ 10 , 23 ], with high prevalence of mental illnesses, intellectual disability, and chronic diseases, such as hypertension, diabetes, and alcoholism [ 19 ].

People living in temporary or crisis accommodation include the following groups:

People in free- or low-cost accommodations and public assistance facilities are mostly beneficiaries of the Public Assistance System which aims to guarantee the minimum standard of living [ 29 , 49 ]. Free- or low-cost accommodations offer accommodation only (with occasional food and consultation services), whereas public assistance facilities provide livelihood assistance and in most cases care for physical or mental disabilities. However, a high prevalence of mental illnesses and intellectual disability was reported among people in free- or low-cost accommodations. Furthermore, economic exploitation by owners of free- or low-cost accommodations and sub-optimal dwelling environment were highlighted [ 10 ].

Self-reliance support centers [ 50 ] are to provide temporary living assistance as stipulated by the Act on Self-reliance Support for Needy Persons, where people are required to find a job and stay for up to 6 months.

The cyber-homeless stay inside internet or comic book cafés, which are open for business 24 h a day and offer not only internet and/or comic book services, but also food, drink, showers, and private rooms. These facilities are often used as affordable temporary accommodation. According to the surveys conducted by the Tokyo Metropolitan Government in 2016, more than a quarter of overnight users were staying these cafés because they had lost homes. Among them, more than 80% were non-regular workers or self-employed individuals. The majority of users are male and lack employment and health insurance [ 33 ]. Some of them also sleep on the streets when needed.

Socio-economic consequences of COVID-19

Loss/reduction of income or difficulty in finding a job due to COVID-19 exerted negative consequences on all groups. People without accommodation gained reduced access to soup and kitchen services offered by NGOs. The suspension of internet cafés resulted in the increase of people without accommodation.

Risk of COVID-19 transmission and progression

All groups classified as people living in temporary or crisis accommodation typically stay in shared spaces. People experiencing loss/reduction of income, particularly those without accommodation and cyber-homeless, are prone to losing health insurance, which increases the difficulty of accessing medical services including COVID-19 testing.

Measures and remaining challenges

Although the Public Assistance System guarantees the minimum standard of living, the take-up rate of Public Assistance reached as low as 20% in Japan [ 51 ]. The contributing factors include social stigma against users, limited awareness of the system, cumbersome application procedures, and strict approval criteria [ 52 ]. Amid the COVID-19 pandemic, the Ministry of Health, Labour and Welfare (MHLW) issued circulars to local governments to streamline the application and approval procedures of the Public Assistance System and to arrange single rooms for individuals who recently lost accommodation. In addition, the government initiated Special Cash Payments amounting to JPY100,000 targeting residents under the Basic Resident Registration System.

The remaining challenges for addressing the socio-economic consequences of COVID-19 and risk of COVID-19 infection and progression include (i) the low utilization of the Public Assistance System and social stigma against users [ 52 ]; (ii) effective and efficient implementation of single room arrangements for individuals who lost accommodation; (iii) prevention practices and/or single room arrangements and other measures for people living in self-reliance support centers, free- or low-cost accommodations, and public assistance facilities; and (iv) access to medical services, particularly among people not covered by health insurance.

Populations that include people living in severely inadequate and insecure accommodation (Table 2 )

Non-regular workers and self-employed individuals including female-parent households include populations with housing instability. The wage for non-regular workers was 65% of that of regular workers in 2018 [ 101 ], whereas the relative poverty rate of single-parent families (mostly female-parents) was 50.8%. Furthermore, non-regular workers are susceptible to termination of employment contract during economic recessions [ 102 ]. The perils of workers are exacerbated by the nearly halved reduction of income [ 103 ] due to the low-level unemployment benefit of employment insurance, particularly for individuals with short work tenure [ 104 ].

The LGBTQ appear to include significant segments of people facing housing instability. A web-based survey that targeted men who have sex with men (MSM) in 2016 revealed that 5.2% out of 6921 respondents had ever lost accommodation, whereas 56.1% experienced worry or stress over income or debt [ 59 ]. Moreover, a survey targeting property owners in 2018 indicated that less than 40% welcome same-sex couples as tenants [ 60 ].

Evacuees from Fukushima after the nuclear disaster in 2011 (internally displaced people) experienced job losses in their home town and were forced to find new jobs as non-regular employees [ 105 ]. Families are separated physically, which results in increased household expenses [ 106 ]. In addition, these workers lost social capital, which is difficult to rebuild in the destination community. Discontinuation of housing assistance as stipulated in the Disaster Relief Act of 2017 affected more than 12,000 households.

Migrants—technical interns (which are foreign nationals in Japan under the Technical Intern Training Program of the Japanese Government), international students, asylum seekers, poverty-stricken long-term residents, and undocumented foreigners have various vulnerabilities. Specifically, technical interns face labor, health, and safety issues; are forced to stay in sub-optimal housing; and are burdened with huge debts in their home countries [ 107 ]. In fact, more than 9000 technical interns have disappeared from designated work places in 2019. International students tend to depend on heavy part-time jobs and have huge debts in their home countries [ 108 ]. Long-term residents, especially those of Japanese descent, work in unstable employment conditions [ 109 ]. Asylum seekers are banned from working for 6 months, and many are detained in immigration detention centers [ 110 ]. Undocumented foreigners, such as ex-technical interns and ex-international students, are excluded from health insurance or social protection schemes [ 111 ].

A large number of adolescents and children roam the streets due to physical and/or mental abuse in their domestic settings [ 112 ]. A total of 230,000 roaming and 160,000 abuse cases were reported in 2018, which have been rapidly increasing in the past decade [ 73 , 113 ]. The prevalent type of abuse was psychological, followed by physical, neglect, and sexual abuse. Abused adolescent girls roaming the streets face various challenges, such as sexual exploitation [ 114 ].

Loss/reduction of income exerted negative consequences on non-regular workers, impoverished LGBTQ, internally displaced people from Fukushima, and foreigners, particularly in industries susceptible to repercussions from the pandemic. Furthermore, each population faces additional difficulties. For example, admission to a facility (i.e., free- or low-cost accommodation) where single rooms are unavailable is traumatic for the LGBTQ, and unemployment of foreign workers leads to loss of residence status and access to social and health services. Finally, staying indoors results in increases in adolescent and child abuse cases [ 115 ].

Despite the stay-at-home policy, people belonging to the five groups are forced to go out for work to earn a living or flee from domestic abuse. People with loss/reduction of income are prone to losing health insurance as they experience difficulties in paying the insurance premium, which may result in delay in accessing medical services including COVID-19 testing. People detained in immigration centers, children and youth in the Child Guidance Center’s Temporary Care Home, and a portion of technical interns are staying in congregated settings. The housing conditions of asylum seekers and undocumented foreigners including ex-technical interns and ex-students remain unclear.

The eligibility criteria for the unemployment benefits of employment insurance have been expanded over time [ 73 ]. However, people who lost jobs, particularly non-regular workers ineligible for unemployment benefits, are prone to losing health insurance. At least 180,000 households are required to pay the full amount of medical cost when using medical services due to delinquency in payment [ 116 ].

As part of urgent measures to respond to the pandemic, the government established the Employment Adjustment Subsidy for employers and the Subsidy Program for Sustaining Businesses for self-employed and freelance workers [ 94 , 117 ]. The former is “provided for an employer, who has been forced to reduce business activities due to the effects of COVID-19 and temporarily suspended work, or who has trained or dispatched employees in order to maintain their employment, for some of the leave allowance paid to employees,” and the latter “targets companies facing severe conditions in particular and provides them with subsidies for a wide variety of purposes that in general are considered effective in supporting them in sustaining or reviving their businesses.” Furthermore, the government initiated Special Cash Payments amounting to JPY100,000 targeting the country’s population registered under the Basic Resident Registration System as eligible recipients.

The remaining challenges common to all groups except for people in abusive domestic settings include providing support for individuals not covered by existing and urgent measures and reducing barriers to such measures. Particularly, assessing the situation of undocumented foreigners is imperative to the development of adequate responses [ 111 ].

For adolescents and children abused in domestic settings, enhancing cooperation among schools, local governments, and child guidance centers during school closure due to COVID-19 is crucial [ 118 ]. To meet the diverse needs of adolescents, such as protection from sexual exploitation and isolation, alternative approaches to existing public institutions should be developed and expanded, which include outreach programs, peer support, and adolescent-sensitive services [ 114 ].

Strategic directions to address challenges

The framework used for the assessment integrated the IGH definition of homelessness and the health, socio-economic, and humanitarian perspectives in line with the COVID-19 operational response strategies for the United Nations (UN) families and partners. While the scope of the assessment could be deemed ambitious, this assessment illustrated a comprehensive yet concise picture of nine groups of populations with unstable housing conditions with diverse backgrounds in Japan.

These populations share a common problem. “Staying at home” is unrealistic because they lack adequate homes as a fundamental human right or are compelled to venture outside to earn a living. Furthermore, the assessment identified a range of major challenges as indicated in Tables 1 and 2 , to wit: increasing utilization of the Public Assistance System, effective and efficient implementation of urgent measures, prevention practices in congregated settings, preventing transmission during work outside home, ensuring timely access to medical services (particularly among individuals not covered by health insurance or other schemes), understanding and addressing the situation of undocumented foreigners, and protecting children and adolescents abused in domestic settings.

To address these challenges, which is a huge task for society, the findings suggest and inspire the following strategies:

Economic damages caused by the pandemic force a large number of populations indicated in Table 2 to confront existing problems faced by the populations summarized in Table 1 . The existing problems include low utilization of the Public Assistance System, and sub-optimal conditions of many self-reliance support centers and free- or low-cost accommodations. With a view to converting crisis into opportunity, it is crucial to highlight and address the existing problems from the viewpoint of people with diverse backgrounds in need of the system and centers due to COVID-19. Specifically, efforts are necessary to engage stakeholders to enhance the implementation and utilization of the Public Assistance System and advocate a safe and private accommodation for all groups.

All groups were deemed to feature vulnerabilities that require vigilant consideration in designing assistance schemes and operationalizing procedures. These vulnerabilities surfaced by the COVID-19 pandemic include traumatic life experiences, mental illnesses, intellectual disability, substance use, gender-related issues, lack of social capital, unstable employment, financial debt, migration, language barriers, and domestic violence.

▪ Each group is not mutually exclusive and may overlap one another. Thus, efforts to address vulnerabilities should be maximized across initiatives that support different groups. For instance, a consortium of NGOs was established in 2019 to gather and pool funding for emergency support for individuals who lack accommodation [ 12 ]. The consortium consisted of NGOs working on various population groups, such as people on streets and in temporary accommodations, adolescent girls in abusive domestic environments, women with pregnancy-related issues, asylum seekers and refugees, poor children and parents, youth in poverty, internally displaced people, and victims of human trafficking, as well as housing support, research and advocacy, and labor issues.

▪ It is also critical to enhance multi-sectoral collaboration in responding to the vulnerabilities of respective groups from health, socio-economic, and humanitarian perspectives [ 14 ]. In particular, the health sector is expected to play an active role to foster such collaboration in addressing the needs of these groups through working with social welfare, labor, industry, education, and other sectors. Initiatives targeting these groups should be advocated and mainstreamed, building on existing initiatives such as Inclusion Health, which is a service, research, and policy agenda that aims to prevent and redress health and social inequities among the most vulnerable and excluded populations such as homeless, prisoners, and drug users [ 119 , 120 ].

Viewing the spectrum of population groups with unstable housing conditions using the proposed framework that encompasses health, socio-economic, and humanitarian perspectives, the COVID-19 pandemic seemingly teaches a lesson, that is, homelessness is not a problem for a specific population but an extension of daily life that many citizens can face with a subtle trigger. A variety of issues that emerged regarding homelessness provided opportunities to recognize and address old and new social determinants of health and to move toward a sustainable symbiotic society in synergy with initiatives for tackling other social issues toward SDGs.

It should be noted that the framework was developed and applied to a wide range of population groups in Japan through consultation with experts and practitioners engaged in unstable housing conditions. However, given that the possibility of overlooking other elusive populations cannot be ruled out, further studies are needed to refine this framework. Considering elusiveness and changeability of vulnerable populations, we hope that this framework can be a reference when planning the comprehensive yet concise assessment of populations with unstable housing in other countries [ 2 ]. The authors wish to share their insights so that partnership and solidarity could be forged across countries [ 121 ].

Limitations

Instead of a systematic manner, data collection was performed according to the framework developed. Thus, its validity and generalizability should be further examined. Although not discussed, other populations exist in Japan, i.e., individuals who are not homeless but significantly susceptible to COVID-19. Such persons include those with disabilities in facilities, the elderly in nursing homes, children in orphanages, prisoners, and psychiatric patients with prolonged hospital stay.

Availability of data and materials

Not applicable, because this article reviews existing literature.

Abbreviations

Coronavirus disease 2019

Global Humanitarian Response Plan

The Institute of Global Homelessness

Lesbian, gay, bisexual, transgender, and queer or questioning

Ministry of Health, Labour and Welfare

Men who have sex with men

Sustainable Development Goals

Strategic Preparedness and Response Plan

United Nations

Ahmed F, Ahmed N, Pissarides C, Stiglitz J. Why inequality could spread COVID-19. Lancet Public Health. 2020;5(5):e240.

Article   Google Scholar  

The Lancet. Redefining vulnerability in the era of COVID-19. The Lancet; 2020: 395(10230):1089.

United Nations Regional Information Center for Western Europe. COVID-19: How do you self-isolate if you have nowhere to live? https://unric.org/en/refugees-and-homeless-covid-19-how-do-you-self-isolate/ . Published 2020. Accessed 16 Oct 2020.

Mosites E, Parker E, Clarke K, Gaeta J, Baggett T, Imbert E, et al. Assessment of SARS-CoV-2 infection prevalence in homeless shelters - four U.S. cities, march 27–April 15, 2020. Morb Mortal Wkly Rep. 2020;69(17):521–2.

Article   CAS   Google Scholar  

Tsai J, Wilson M. COVID-19: a potential public health problem for homeless populations. In., vol. 5: The Lancet Public Health. 2020:5(4):e186-87.

Aldridge R, Story A, Hwang S, Nordentof M, Luchensk S, Hartwell G, et al. Morbidity and mortality in homeless individuals, prisoners, sex workers, and individuals with substance use disorders in high-income countries: a systematic review and meta-analysis. Lancet. 2017;391(10117):241–50.

FEANTSA. COVID-19: “staying home” not an option for people experiencing homelessness. https://www.feantsa.org/en/news/2020/03/18/covid19-staying-home-not-an-option-for-people-experiencing-homelessness . Published 2020. Accessed 16 Oct 2020.

United Nations Human Settlements Programme. Homelessness & the SDGs. https://www.un.org/development/desa/dspd/wp-content/uploads/sites/22/2019/07/SALCEDO_Jesus_Presentation_2-1.pdf . Published 2019. Accessed 16 Oct 2020.

Busch-Geertsema V, Culhane D, Fitzpatrick S. Developing a global framework for conceptualising and measuring homelessness. Habitat International. 2016;55:124–32.

Ministry of Health, Labour and Welfare. National survey on the actual conditions of the homeless (Headcount Survey), 2019. (in Japanese) https://www.mhlw.go.jp/content/12003000/000505478.pdf . Published 2019. Accessed 16 Oct 2020.

Ohtsu T, Toda R, Shiraishi T, Toyoda H, Toyozawa H, Kamioka Y, et al. Living conditions, ability to seek medical treatment, and awareness of health conditions and healthcare options among homeless persons in Tokyo, Japan. Acta Med Okayama. 2011;65(6):387–93.

PubMed   Google Scholar  

Inaba T. Open the closed door, social action against exclusion and poverty (in Japanese). Asahi Shimbun Publications Inc. Published 2020.

Akazawa S. Net Cafe Refugees The coronavirus pandemic and Japan's homeless population. https://metropolisjapan.com/net-cafe-refugees/ . Published 2020. Accessed 16 Oct 2020.

United Nations. Shared responsibility, global solidarity: responding to the socio-economic impacts of COVID-19. https://unsdg.un.org/sites/default/files/2020-03/SG-Report-Socio-Economic-Impact-of-Covid19.pdf . Published 2020. Accessed 16 Oct 2020.

United Nations. United Nations comprehensive response to COVID-19: saving lives, protecting societies, recovering better. https://unsdg.un.org/sites/default/files/2020-06/UN-Response-to-COVID-19.pdf . Published 2020. Accessed 16 Oct 2020.

World Health Organization. 2019 novel coronavirus (2019-nCoV): strategic preparedness and response plan. https://www.who.int/docs/default-source/coronaviruse/srp-04022020.pdf . Published 2020. Accessed 16 Oct 2020.

United Nations. An UN framework for the immediate socio-economic response to COVID-19. https://unsdg.un.org/sites/default/files/2020-04/UN-framework-for-the-immediate-socio-economic-response-to-COVID-19.pdf . Published 2020. Accessed 16 Oct 2020.

UN Office for the Coordination of Humanitarian Affairs. Global Humanitarian Response Plan: COVID-19 (April – December 2020) GHRP July Update. https://reliefweb.int/sites/reliefweb.int/files/resources/GHRP-COVID19_July_update_0.pdf . Published 2020. Accessed 16 Oct 2020.

Nishio A, Yamamoto M, Ueki H, Watanabe T, Matsuura K, Tamura O, et al. Prevalence of mental illness, intellectual disability, and developmental disability among homeless people in Nagoya, Japan: a case series study. Psychiatry Clin Neurosci. 2014;69(9):534–42.

Google Scholar  

Bureau of Social Welfare and Public Health, Tokyo Metropolitan Government. Report on actual conditions of people with precarious work and housing. (in Japanese) https://www.metro.tokyo.lg.jp/tosei/hodohappyo/press/2018/01/26/documents/14_02.pdf . Published 2018. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. Survey on the conditions of Self-reliance Support for Needy Persons, 2018. (in Japanese) https://www.mhlw.go.jp/content/000542747.pdf . Published 2018. Accessed 16 Oct 2020.

National Institute of Population and Social Security Research. Population and Social Security in Japan. http://www.ipss.go.jp/s-info/e/pssj/pssj2019.pdf . Published 2019. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. National survey on the actual conditions of the homeless, 2016. (in Japanese) https://www.mhlw.go.jp/file/04-Houdouhappyou-12003000-Shakaiengokyoku-Shakai-Chiikifukushika/02_homeless28_kekkasyousai.pdf . Published 2017. Accessed 16 Oct 2020.

Advocacy and Research Centre for Homelessness. 2019 Summer Tokyo Street Count. https://www.archomelessness.org/ . Updated 2020. Accessed 16 Oct 2020.

Morikawa S, Uehara R, Okuda K, Shimizu H, Nakamura Y. Prevalence of psychiatric disorders among homeless people in one area of Tokyo (in Japanese). Jap J Public Health. 2011;58(5):331–9.

Siripala T. Tokyo’s homeless pressured to disappear ahead of olympics. https://thediplomat.com/2020/02/tokyos-homeless-pressured-to-disappear-ahead-of-olympics/ . Updated 2020. Accessed 16 Oct 2020.

Shirakawa M. Homeless denied shelter during Typhoon Hagibis https://www3.nhk.or.jp/nhkworld/en/news/backstories/731/ . Updated 2019. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. Survey on facilities that offer free or low-cost accommodations. (in Japanese) https://www.mhlw.go.jp/file/04-Houdouhappyou-12002000-Shakaiengokyoku-Shakai-Hogoka/0000134574.pdf . Published 2015. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. Shelters based on Public Assistance Act. (in Japanese) https://www.mhlw.go.jp/shingi/2002/05/s0510-2e.html . Published 2002. Accessed 16 Oct 2020.

Endo M, Ota M, Kayebeta A, Takahashi I, Nagata Y. A tuberculosis outbreak at an insecure, temporary housing facility, manga café, Tokyo, Japan, 2016–2017. Epidemiol Infect. 2019;144(e222):1-6. doi: https://doi.org/ https://doi.org/10.1017/S0950268819001092 .

23 Special Wards of Tokyo. Support measures for rough sleepers. (in Japanese) http://www.tokyo23city.or.jp/ki/kyodo/ki3-9.html . Accessed 16 Oct 2020.

IROIRO. A list of self-reliance support centers in Tokyo 2017-2019. (in Japanese) https://iroiro.co/article/justice/society/homeless-center/ . Updated 2018. Accessed 16 Oct 2020.

Bureau of Social Welfare and Public Health, Tokyo Metropolitan Government. Report on actual conditions of people with precarious work and housing. (in Japanese) https://www.metro.tokyo.lg.jp/tosei/hodohappyo/press/2018/01/26/documents/14_02.pdf . Publisehd 2018. Accessed 16 Oct 2020.

Kizuki M, Takano T, Nakamura K, Fukuda Y, Watanabe M, Inose T, et al. Social course patterns of urban dwellers with tuberculosis under fragile living conditions in Tokyo, Japan. J Epidemiol. 2006;16(4):167–75.

SankeiBiz. Big issue, supporting homeless people, faced by a shrink in sales amid the novel coronavirus pandemic. Secretariat considering remedy measures. (in Japanese) https://www.sankeibiz.jp/business/news/200504/bsm2005040655001-n1.htm . Updated 2020. Accessed 16 Oct 2020.

Takikawa D. Tokyo’s homeless hit hard as support groups stop giving out food, masks over virus fears. The Mainichi. https://mainichi.jp/english/articles/20200403/p2a/00  m/0na/027000c. Updated 2020. Accessed 16 Oct 2020.

David HS, Ikebe S. Social distancing from the problem of Japanese homelessness under covid-19. Asia-Pacific J. 2020;18(18):4.

The Mainichi. Prompt support’ needed as the agony spreading from day laborers: asking Ren Onishi, NPO MOYAI, about the current situations of the poor. (in Japanese) https://mainichi.jp/articles/20200411/k00/00  m/040/027000c. Updated 2020. Accessed 16 Oct 2020.

Saito B. For Tokyo poor living in net cafes, virus state of emergency could mean homelessness. The Mainichi. https://mainichi.jp/english/articles/20200409/p2a/00  m/0na/037000c. Updated 2020. Accessed 16 Oct 2020.

Chandran R. Tokyo faces pressure to house internet cafe homeless. JAPANTODAY. https://japantoday.com/category/national/Tokyo-faces-pressure-to-house-internet-cafe-homeless . Updated 2020. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. Public Assistance System. https://www.mhlw.go.jp/english/topics/social_welfare/dl/outline_of_the_public_assistance_system_20101004.pdf . Published 2010. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. About Self-reliance Support System for Needy Persons. (in Japanese) https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000073432.html . Updated 2020. Accessed 16 Oct 2020.

Tokyo Metropolitan Government. Tokyo Challenge Net. (in Japanese) https://www.tokyo-challenge.net/ . Updated 2020. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. Correspondence to provision of public assistance services, for the prevention of spreading the novel coronavirus. (in Japanese) https://www.mhlw.go.jp/content/000619973.pdf . Published 2020. Accessed 18 Oct 2020.

Ministry of Health, Labour and Welfare. Correspondence to the declaration of the state of emergency regarding the novel coronavius pandemic. (in Japanese) https://www.mhlw.go.jp/content/000622762.pdf . Published 2020. Accessed 18 Oct 2020.

Ministry of Health, Labour and Welfare. Correspondence to the infection control practices of the novel coronavirus at free or low-cost accommodation. (in Japanese) https://www.mhlw.go.jp/content/000621768.pdf . Published 2020. Accessed 18 Oct 2020.

Ministry of Health, Labour and Welfare. The utilization of temporary housing support services under the self-reliance support law for the needy persons. (in Japanese) https://www.mhlw.go.jp/content/000621870.pdf . Published 2020. Accessed 18 Oct 2020.

Japan Federation of Bar Associations. Public Assistance System, Q&A brochure. (in Japanese) https://www.nichibenren.or.jp/library/ja/publication/booklet/data/seikatuhogo_qa.pdf . Accessed 18 Oct 2020.

Tsujii M. Research survey on support needs assessment of the users who require daily living suports at free- or low-cost accommodations. (in Japanese) Ministry of Health, Labour and Welfare. https://www.mhlw.go.jp/content/12200000/000525955.pdf . Published 2018. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. Partial Revision of the Act on Self-reliance Support for Needy Persons to Promote Independence of Needy Persons. https://www.mhlw.go.jp/english/policy/care-welfare/social-welfare/dl/201904shakai.pdf . Published 2019. Accessed 16 Oct 2020.

Sai E. Problems and future perspectives of the public assistance system. (in Japanese) The Journal of Economic Policy Studies, Kagawa University; 2015;11:159-78. https://www.ec.kagawa-u.ac.jp/~tetsuta/jeps/no11/sai.pdf . Accessed 16 Oct 2020.

Hara S. Poverty and Public Assistance System (49) Low take-up rate as a serious problem of public assistance system. (in Japanese) https://yomidr.yomiuri.co.jp/article/20170630-OYTET50005/ . Published 2017. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. Transition of rate/number of non-regular workers who reluctantly took non-regular jobs. (in Japanese) https://www.mhlw.go.jp/wp/hakusyo/roudou/18/backdata/1-2-16.html . Updated 2018. Accessed 18 Oct 2020.

Ministry of Health, Labour and Welfare. Basic Survey on Wage Structure, 2018. (in Japanese) https://www.mhlw.go.jp/toukei/itiran/roudou/chingin/kouzou/z2018/index.html . Updated 2018. Accessed 18 Oct 2020.

Ministry of Health, Labour and Welfare. Report on Single Parent Family, 2016. (in Japanese) https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000188147.html . Updated 2016. Accessed 18 Oct 2020.

Ministry of Health, Labour and Welfare. Support for single parent family. (in Japanese) https://www.mhlw.go.jp/bunya/kodomo/pdf/shien_01.pdf . Published 2014. Accessed 18 Oct 2020.

Ministry of Health, Labour and Welfare. Graphical review of Japanese household: from comprehensive survey of living conditions. (in Japanese) https://www.mhlw.go.jp/toukei/list/dl/20-21-h28.pdf . Published 2016. Accessed 18 Oct 2020.

Japan LGBT Research Institute Inc. Report of perception and behavior of LGTB. (in Japanese) https://www.daiko.co.jp/dwp/wp-content/uploads/2019/11/191126_Release.pdf . Published 2019. Accessed 18 Oct 2020.

LASH. LASH report 2017. (in Japanese) https://lash.online/wp/wp-content/uploads/2018/02/LASH.pdf . Published 2018. Accessed 16 Oct 2020.

Recruit Sumai Company. Survey on residential life and housing of LGBT. (in Japanese) https://www.recruit-sumai.co.jp/data/upload/SUUMO_LGBT_cyosa_20181004.pdf . Published 2018. Accessed 16 Oct 2020.

Amnesty International. Human rights law and discrimination against LGBT people in Japan. https://www.amnesty.org/download/Documents/ASA2259552017ENGLISH.PDF . Published 2017. Accessed 18 Oct 2020.

Fukushima Prefectural Government, Japan. Situation of evacuation from Fukushima prefecture to other prefectures. (in Japanese) https://www.pref.fukushima.lg.jp/site/portal/ps-kengai-hinansyasu.html . Updated 2020. Accessed 19 Oct 2020.

Fukushima on the Globe. Situation of the Evacuees. http://fukushimaontheglobe.com/the-earthquake-and-the-nuclear-accident/situation-of-the-evacuees . Updated 2020. Accessed 19 Oct 2020.

Niigata Prefectural Government, Japan. Comprehensive research report on life of evacuees affected by the accident of Fukushima No.1 Nuclear Power Plant. (in Japanese) https://www.pref.niigata.lg.jp/uploaded/attachment/93775.pdf . Published 2018. Accessed 19 Oct 2020/.

Yabuki R, Kawasaki K. The actual state and wishes of voluntary evacuees after the end of provision of the temporary housing - a study of voluntary evacuees from Fukushima Prefecture affected by the Fukushima nuclear accident -. (in Japanese) Reports of the City Planning Institute of Japan. 2018;17. https://www.cpij.or.jp/com/ac/reports/17_1.pdf . Accessed 19 Oct 2020.

NHK. The accident of Fukushima No.1 Nuclear Power Plant; What is happening to mental health of evacuees?- Results of a large-scale survey in 9 years time. (in Japanese) https://www.nhk.or.jp/gendai/articles/4396/index.html . Updated 2020. Accessed 19 Oct 2020.

Ministry of Justice. Regarding the issues of absconding during technical intern training. (in Japanese) http://www.moj.go.jp/content/001311268.pdf . Published 2019. Accessed 19 Oct 2020.

Japan Association for Refugees. Lacking ‘housing’ safety net. (in Japanese) https://www.refugee.or.jp/jar/report/2017/03/17-0000.shtml . Updated 2017. Accessed 19 Oct 2020.

Yang L. Labor segmentation and the outmigration intention of highly skilled foreign workers: evidence from Asian-born foreign workers in Japan. The Research Institute of Economy, Trade and Industry. RIETI Discussion Paper Series 18-E-028. https://www.rieti.go.jp/jp/publications/dp/18e028.pdf . Published 2019. Accessed 19 Oct 2020.

Ministry of Justice. Handling of applications for the status of residence of technical interns who have been affected by the novel coronavirus pandemic. (in Japanese) http://www.moj.go.jp/nyuukokukanri/kouhou/nyuukokukanri07_00026.html . Updated 2020. Accessed 19 Oct 2020.

Kashida H. Don’t foreigners without resident status have human rights? (in Japanese) https://imidas.jp/jijikaitai/c-40-132-19-09-g725 . Updated 2019. Accessed 19 Oct 2020.

Ministry of Justice. Transition of the number of foreign residents by resident status. (in Japanese) http://www.moj.go.jp/content/001308162.pdf . Published 2019. Accessed 19 Oct 2020.

National Police Agency. Protection and guidance of the youth in 2019. (in Japanese) https://www.npa.go.jp/safetylife/syonen/hodouhogo_gaikyou/R01.pdf . Published 2019. Accessed 16 Oct 2020.

Think Kids. The impact of child abuse on children and the need for healing their emotional scar. (in Japanese) [ http://www.thinkkids.jp/genjou/reason ]. Updated 2020. Accessed 19 Oct 2020.

Ministry of Health, Labour and Welfare. The number of counseling on and response to child abuse at child guidance centers in 2018. (in Japanese) https://www.mhlw.go.jp/content/11901000/000533886.pdf . Published 2018. Accessed 19 Oct 2020.

Tsuboi S, Yoshida H, Ae R, Kojo T, Nakamura Y, Kitamura K. Prevalence and demographic distribution of adult survivors of child abuse in Japan. Asia Pac J Public Health. 2013;27(2):NP2578–86.

Nito Y. Girls’ sexual exploitation, poverty, and isolation in Japan: raising our voice with junior and senior high school girls. (in Japanese). J Poverty. 2018;21.

Osumi M. For Japan’s nonregular workers, a weaker safety net as COVID-19 spreads. The Japan Times. https://www.japantimes.co.jp/news/2020/03/31/national/nonregular-workers-japan-coronavirus/#.Xr0LIWj7SUk . Updated 2020. Accessed 19 Oct 2020.

SankeiBiz. Single parent-continuouse school closure and tenuous day-to-day life. (in Japanese) https://www.sankeibiz.jp/econome/news/200313/ecb2003132110007-n1.htm . Updated 2020. Accessed 19 Oct 2020.

The Japan Times. LGBT people in Japan worry getting coronavirus may result in outing. https://www.japantimes.co.jp/news/2020/04/21/national/social-issues/lgbt-coronavirus/ . Updated 2020. Accessed 19 Oct 2020.

Concerns on further isolation of victims of the earthquake disaster and nuclear accident- suspension of regular exchange events to avoid the “3 Cs” [ https://www.kahoku.co.jp/tohokunews/202004/20200419_73012.html (in Japanese)].

Slater DH. Vulnerable populations under COVID-19 in Japan: a lull in the storm? Asia-Pacific Journal. 2020;18(18):2.

Tran BQ. Vietnamese technical trainees in Japan voice concerns admidst COVID-19. Asia-Pacific Journal. 2020;18(18):11.

Nikkei. 10 - 20 percent extra child abuse, covid-19 impact study by Ministry of Health, Labour and Welfare. (in Japanese) https://www.nikkei.com/article/DGXMZO58982720S0A510C2000000/ . Updated 2020. Accessed 19 Oct 2020.

The Mainichi. Editorial: Efforts needed to detect violence at home as Japan asks people to stay indoors. https://mainichi.jp/english/articles/20200422/p2a/00  m/0na/014000c. Updated 2020. Accessed 19 Oct 2020.

PERSOL. Urgent research on the impact of COVID-19 response on telework. (in Japanese) https://rc.persol-group.co.jp/research/activity/data/telework.html . Updated 2020. Accessed 19 Oct 2020.

MIN-IREN. Study report on fetal accident due to fainancial chllenges in 2018. (in Japanese) https://www.min-iren.gr.jp/wp-content/uploads/2018/06/190306_04.pdf . Published 2018. Accessed 19 Oct 2020.

South China Morning Post. Chinese intern found dead in company dormitory in Japan and police suspect his co-worker killed him. https://www.scmp.com/news/asia/east-asia/article/2185267/chinese-intern-found-dead-company-dormitory-japan-and-police . Updated 2020. Accessed 19 Oct 2020.

Nikkei. Voluntary ban on leaving home and concerns on DV and abuse - a wave of counselings, need an urgent response. (in Japanese) https://www.nikkei.com/article/DGXMZO57949360Q0A410C2CC1000/ . Updated 2020. Accessed 19 Oct 2020.

Matsuyama S. “It was ike a prison” A story of a woman previousely taken into temporary custody facility. (in Japanese) The Asahi Shimbun. https://www.asahi.com/articles/ASM4Z3CSWM4ZUDCB001.html . Updated 2020. Accessed 19 Oct 2020.

Ministry of Health, Labour and Welfare. Employment insurance system. (in Japanese) https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/koyou_roudou/koyou/koyouhoken/index_00003.html . Updated 2020. Accessed 19 Oct 2020.

Ministry of Health, Labour and Welfare. Employment insurance is available for daily workers. (in Japanese) https://www.mhlw.go.jp/bunya/koyou/koyouhoken07/hoken.html . Updated 2020. Accessed 19 Oct 2020.

Ministry of Health, Labour and Welfare. Outline of the Act on the Partial Amendment to the Child Welfare, etc. to step up efforts to prevent child abuse (Act No. 46 of 2019) https://www.mhlw.go.jp/english/policy/children/children-childrearing/dl/20191122e.pdf . Published 2019. Accessed 19 Oct 2020.

Ministry of Health, Labour and Welfare. Employment adjustment subsidy. (in Japanese) https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/koyou_roudou/koyou/kyufukin/pageL07.html . Published 2020. Accessed 16 Oct 2020.

Ministry of Economy, Trade and Industry. Subsidy Program for Sustaining Businesses Starts. (in Japanese) https://www.meti.go.jp/covid-19/jizokuka-kyufukin.html . Updated 2020. Accessed 19 Oct 2020.

Ministry of Justice. Regarding response to residence application in the context of the spread of CIVID-19 infection. (in Japanese) http://www.moj.go.jp/nyuukokukanri/kouhou/nyuukokukanri07_00026.html . Updated 2020. Accessed 19 Oct 2020.

Ministry of Health, Labour and Welfare. Regarding the implementation of the “Action plan for strengthening monitoring of children”. (in Japanese) https://www.mhlw.go.jp/content/000625822.pdf . Published 2020. Accessed 19 Oct 2020.

Kambayashi T. Japanese angered by PM’s stay-at-home video featuring dog and song. Hdhod. https://en.hdhod.com/Japanese-angered-by-PM-s-stay-at-home-video-featuring-dog-and-song_a20665.html . Updated 2020. Accessed 19 Oct 2020.

DeWit A, Shaw R, Djalante R. An integrated approach to sustainable development, National Resilience, and COVID-19 responses: the case of Japan. Int J Disaster Risk Reduct. 2020;51:101018.

Larochelle MR. “Is it safe for me to go to work?” Risk stratification for workers during the covid-19 pandemic. New Engl J Med . 2020;383(5):e28(21-23).

Ministry of Health, Labour and Welfare. Overview of the basic survey on wage structure, 2018. (in Japanese) https://www.mhlw.go.jp/toukei/itiran/roudou/chingin/kouzou/z2018/index.html . Published 2018. Accessed 16 Oct 2020.

Naito H. Massive lay-off of contractual workers (May Crisis ahead) with Government not understanding the real situation. (in Japanese) https://www.asahi.com/articles/ASN5K65ZVN5KULFA00M.html . Published 2020. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. For those who receive basic benefit (unemployment benefit) of employment insurance. (in Japanese) https://www.mhlw.go.jp/content/000489683.pdf . Published 2020. Accessed 16 Oct 2020.

Tachibanaki T. A beginner’s book on social security (in Japanese). Minerva Shobo. Published 2019.

Niigata Prefectural Government. Comprehensive research report on life of evacuees affected by the accident No.1 Nuclear Power Plant. (in Japanese) https://www.pref.niigata.lg.jp/uploaded/attachment/93775.pdf . Published 2018. Accessed 16 Oct 2020.

Yabuki R, Kawasaki K. The actual state and wishes of voluntary evacuees after the end of provision of the temporary housing - a study of voluntary evacuees from Fukushima Prefecture affected by the Fukushima nuclear accident-. (in Japanese) Reports of the City Planning Institute of Japan. 2018;17:1-7. https://www.cpij.or.jp/com/ac/reports/17_11.pdf . Accessed 16 October.

Ministry of Justice. Regarding the issues of absconding during technical intern training. (in Japanese) http://www.moj.go.jp/content/001311268.pdf . Published 2019. Accessed 16 Oct 2020.

Idei Y. Money dreams: foreign students to Japan face growing risks. https://www.nippon.com/en/currents/d00340/ . Published 2017. Accessed 16 Oct 2020.

Higuchi N. The mass employment of Brazilians in Japan under the economic crisis. (in Japanese) journal of Ohara institute for. Soc Res. 2010;622:50–66.

Human Rights Now. Statement calling for the prohibition of arbitrary detention in immigration facilities and the improvement of legal measures. http://hrn.or.jp/eng/news/2019/10/31/immigration_reform_statement/ . Published 2019. Accessed 16 Oct 2020.

Parikh NS. Migrant health in Japan: safety-net policies and advocates’ policy solutions. Asia-Pac J. 2010;8(12):3.

Think Kids. The impact of child abuse on children and the need for healing their emotiona scar. (in Japanese) http://www.thinkkids.jp/genjou/reason . Updated 2020. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. The number of counseling on and response to child abuse at child guidance centers in 2018. (in Japanese) https://www.mhlw.go.jp/content/11901000/000533886.pdf . Published 2018. Accessed 16 Oct 2020.

Nito Y. Girls’ sexual exploitation, poverty, and isolation in Japan: raising our voice with junior and senior high school girls. (in Japanese) J Poverty 2018;(21):92-104.

Ministry of Health, Labour and Welfare. The number of calls of Child Abuse Hotline (189) by Child Guidance Center. (in Japanese) https://www.mhlw.go.jp/content/000628644.pdf . Published 2020. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. Financial balance of National Health Insurance (municipalities) in 2018. (in Japanese) https://www.mhlw.go.jp/file/04-Houdouhappyou-12401000-Hokenkyoku-Soumuka/0000153105_1.pdf . Published 2018. Accessed 16 Oct 2020.

Ministry of Economy, Trade and Industry. METI’s support measures for companies concerning the impacts of the novel coronavirus disease. https://www.meti.go.jp/english/covid-19/index.html#10 . Updated 2020. Accessed 16 Oct 2020.

Ministry of Health, Labour and Welfare. Action plan for strengthening of watching over children. (in Japanese) https://www.mhlw.go.jp/content/000625488.pdf . Published 2020. Accessed 16 Oct 2020.

Edge C, Story A, Hayward A. Inclusion health and COVID-19. https://blogs.ucl.ac.uk/iehc/2020/05/13/inclusion-health-and-covid-19/ . Updated 2020. Accessed 16 Oct 2020.

Marmot M. Inclusion health: addressing the causes of the causes. Lancet. 2018;391(10117):186–8.

Nagai M, Oikawa M, Tamura T, Egami Y, Fujita N. Can we apply lessons learned from Ebola experience in West Africa for COVID-19 in lower income countries? Global Health Med. 2020;2(2):140–1.

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Acknowledgements

The authors acknowledge the contribution of the following individuals who provided critical comments to an earlier version of this manuscript: Akiko Takeishi, Kenji Kubota, Kenji Seino, Tsuyoshi Inaba, Takashi Sawada, Hiroki Mochizuki, Tsutomu Yamanaka, Yuko Yoneda, Hiroshi Miyake, Mayumi Ohnishi, Minju Yoshimoto, Shinsuke Miyano, Miwa Kanda, Hidechika Akashi, Chiaki Miyoshi, Azusa Iwamoto, Shinichiro Noda, Tomoyo Miyake, Khuat Thi Hai Oanh, and Saul Helfenbein.

This paper is supported by Special Fund for Addressing the Novel Coronavirus Disease of National Center for Global Health and Medicine (NCGM).

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Fujita, M., Matsuoka, S., Kiyohara, H. et al. “Staying at home” to tackle COVID-19 pandemic: rhetoric or reality? Cross-cutting analysis of nine population groups vulnerable to homelessness in Japan. Trop Med Health 48 , 92 (2020). https://doi.org/10.1186/s41182-020-00281-0

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Evaluating the impact of stay-at-home and quarantine measures on COVID-19 spread

• Renquan Zhang 1 ,
• Yu Wang 1 ,
• Zheng Lv 2 &
• Sen Pei 3  

BMC Infectious Diseases volume  22 , Article number:  648 ( 2022 ) Cite this article

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During the early stage of the COVID-19 pandemic, many countries implemented non-pharmaceutical interventions (NPIs) to control the transmission of SARS-CoV-2, the causative pathogen of COVID-19. Among those NPIs, stay-at-home and quarantine measures were widely adopted and enforced. Understanding the effectiveness of stay-at-home and quarantine measures can inform decision-making and control planning during the ongoing COVID-19 pandemic and for future disease outbreaks.

In this study, we use mathematical models to evaluate the impact of stay-at-home and quarantine measures on COVID-19 spread in four cities that experienced large-scale outbreaks in the spring of 2020: Wuhan, New York, Milan, and London. We develop a susceptible-exposed-infected-removed (SEIR)-type model with components of self-isolation and quarantine and couple this disease transmission model with a data assimilation method. By calibrating the model to case data, we estimate key epidemiological parameters before lockdown in each city. We further examine the impact of stay-at-home and quarantine rates on COVID-19 spread after lockdown using counterfactual model simulations.

Results indicate that self-isolation of susceptible population is necessary to contain the outbreak. At a given rate, self-isolation of susceptible population induced by stay-at-home orders is more effective than quarantine of SARS-CoV-2 contacts in reducing effective reproductive numbers \(R_e\) . Variation in self-isolation and quarantine rates can also considerably affect the duration of outbreaks, attack rates and peak timing. We generate counterfactual simulations to estimate effectiveness of stay-at-home and quarantine measures. Without these two measures, the cumulative confirmed cases could be much higher than reported numbers within 40 days after lockdown in Wuhan, New York, Milan, and London.

Conclusions

Our findings underscore the essential role of stay-at-home orders and quarantine of SARS-CoV-2 contacts during the early phase of the pandemic.

Peer Review reports

Emerged in late 2019, a new respiratory pathogen, SARS-CoV-2, rapidly spread across the globe and caused a global pandemic. As of June 1, 2022, more than 527 million confirmed cases have been reported worldwide, of which more than 6.2 million have died [ 1 ]. The disease caused by SARS-CoV-2, the coronavirus disease 2019 (COVID-19), is characterized by a substantial proportion of infections with mild or no symptoms [ 2 ] and a strong age gradient in the risk of death [ 3 , 4 ]. During the early stage of the COVID-19 outbreak, the number of confirmed cases generally followed an exponential increase. Studies have shown that the average estimate of the basic reproductive number \(R_0\) is between 2.24 and 3.58 [ 5 ]. After China implemented strict control measures, the spread of COVID-19 within China was greatly reduced [ 6 , 7 , 8 , 9 , 10 ]. In other countries, after the initial reporting of infection cases, NPIs such as suspension of classes, cessation of large-scale gatherings, and closure of entertainment and leisure venues have been adopted. These control measures were estimated to effectively slow down the community transmission of SARS-CoV-2 [ 11 , 12 , 13 ].

Before the development of vaccine and its wide administration, NPIs are the primary means to reduce the spread of SARS-CoV-2 [ 14 , 15 , 16 ]. During vaccination campaign, NPIs also remain key in reducing infections [ 17 , 18 ]. Among the implemented NPIs, stay-at-home orders were announced to encourage self-isolation of all population to reduce potential contacts with infectious individuals. In parallel, quarantine was used to separate individuals who have been exposed to COVID-19 from others, which prevents spread of COVID-19 that can occur before a person knows they are infected. During the early days of the COVID-19 pandemic, case isolation and contact tracing were employed to contain the outbreak; however, for an infectious disease whose infectiousness begins before symptoms appear, the effectiveness of isolating cases and tracing contacts is limited [ 19 , 20 , 21 ]. Indeed, a study found that, for the Lombardy ICU network in Italy, strict self-quarantine measures may be the only possible way to contain the spread of infection [ 22 ]. As a result, understanding the impact of self-isolation induced by stay-at-home orders and quarantine of SARS-CoV-2 contacts on COVID-19 spread and the intensity of these measures required to contain an outbreak is critical for planning control measures by governments and public health authorities.

In this study, we developed a mathematical model to estimate the effect of stay-at-home and quarantine on suppressing COVID-19 spread in four cities: Wuhan in China, New York City in the US, Milan in Italy, and London in the UK. Those cities experienced early outbreaks of COVID-19 and all enforced strict interventions to control the transmission of SARS-CoV-2. We incorporated components of self-isolation and quarantine into a classical susceptible-exposed-infected-removed (SEIR) model, and calibrated the model to confirmed cases in each city during the early phase of the pandemic using a data assimilation method. We estimated time-varying key epidemiological parameters before lockdown in each city, and evaluated the impact of the isolation rates of susceptible, exposed and undetected infected populations on disease transmission. Particularly, we estimated the required minimal self-isolation and quarantine rates of those populations to reduce the effective reproductive number below 1 at the beginning of lockdown. We further simulated counterfactual outbreaks within 40 days following lockdown, assuming no stay-at-home and quarantine were implemented in those cities, and estimated the averted cases attributed to these two measures. Overall, stay-at-home and quarantine measures have effectively prevented 3,589,622, 3,281,480, 629,046 and 2,452,750 reported cases during this 40-day period in Wuhan, New York, Milan, and London, respectively. In other words, without these two measures, the cumulative cases during the 40-day period could be 71, 21, 41, and 99 times higher than the reported cases in these four cities.

We used a modified SEIR model to depict the transmission of SARS-CoV-2 in a location with quarantine measures. The model dynamics is shown in Fig. 1 . Specifically, S , E , \(S_q\) , \(E_q\) , \(I_r\) , \(I_u\) , \(I_q\) and R represent susceptible, exposed, self-isolated susceptible, quarantined exposed, reported infected, unreported infected, isolated infected and removed (recovered or dead) populations. A susceptible individual can be infected by a reported infection with a transmission rate \(\beta\) or an unreported infection with a transmission rate \(\mu \beta\) where \(\mu \in [0,1]\) . Note we assume undocumented infections are less contagious than confirmed cases, as indicated in previous studies [ 23 , 24 , 25 , 26 ]. Exposed individuals become contagious after a mean latency period of L days. A fraction of infected population, \(\alpha\) , is ascertained as confirmed cases. Infected individuals recover or die after a mean infectious period of D days.

We assume susceptible population is self-isolated by a rate \(q_0\) , representing the effect of stay-at-home orders. Quarantine of SARS-CoV-2 contacts has little impact on the overall self-isolation rate of susceptible population during the early pandemic as the percentage of exposed contacts is negligible compared to the total population. For instance, in a city with millions of residents, a few thousand exposed contacts only account for less than 0.1% of total population. We therefore neglect the effect of quarantine of SARS-CoV-2 contacts on susceptible population. We assume the self-isolation rate \(q_0\) for susceptible population does not depend on infectious population as the proportion of total population who have contacts with infections is negligible. This assumption simplifies the model structure and does not significantly affect the results. For exposed individual, we assume the quarantine rate is \(q_1\) , representing the effect of quarantine. Undocumented infections are isolated following a different isolation rate \(q_2\) to reflect the differential perception of infection risk as they may have mild symptoms. In total, we define three separate rates \(q_0\) , \(q_1\) and \(q_2\) for susceptible, exposed, and undocumented infections respectively. Confirmed cases are isolated following an isolation rate \(q_3\) after they become infectious. Self-isolated susceptible people are released from self-isolation after an average of Q days. We assume self-isolated or quarantined individuals (susceptible, exposed or infected) do not participate in disease transmission. The transmission dynamics is described by the following equations:

Using model equations, we compute the effective reproductive number, \(R_e\) , i.e. the average number of new infections caused by a single infected individual in a population with partial immunity, as

In model simulations, we deterministically integrate equations using the 4th-order Runge-Kutta method.

Model calibration before lockdown

We calibrated the transmission model to daily confirmed cases in each city using a data assimilation method - the ensemble adjustment Kalman filter (EAKF) [ 27 ]. We chose to use case data because they are available for most locations as a standard surveillance target. To account for case underreporting, we explicitly modeled undocumented infections in our model, which can partially alleviate the effect of limited testing resources. Hospitalization data are less impacted by underreporting; however, hospitalization data have a longer delay compared with case data and are more biased to older and vulnerable population. To match infection to hospitalization in the transmission model, an additional infection-hospitalization rate (IHR) needs to be defined. This IHR may vary in different locations due to different levels of healthcare capacity and could further complicate the model.

The EAKF is a recursive filtering technique that assimilates observations into a dynamic model to generate a posterior estimate of model state (both parameters and variables). Importantly, the EAKF can estimate time-varying parameters, as model parameters are updated daily once new information of confirmed cases is available. This capability is critical for this study because model parameters such as the transmission rate and ascertainment rate may change over time due to varying control measures and testing availability. In this study, we estimated the posterior distributions of model parameters for each day, reflecting the shifting situation during the early pandemic. The EAKF has been widely used in numerical weather prediction [ 27 , 28 ] as well as inference and forecasting of infectious diseases such as influenza [ 29 , 30 , 31 , 32 , 33 ], COVID-19 [ 34 , 35 , 36 , 37 , 38 ], other respiratory viruses [ 39 ], and antimicrobial-resistant pathogens [ 40 , 41 ].

The EAKF assumes a Gaussian distribution of both the prior and likelihood and adjusts the prior distribution to a posterior using Bayes’ rule. To represent the state-space distribution, the EAKF maintains an ensemble of system state vectors acting as samples from the distribution. In particular, the EAKF assumes that both the prior distribution and likelihood are Gaussian, and thus can be fully characterized by their first two moments (mean and variance). The update scheme for ensemble members is computed using Bayes’ rule (posterior \(\propto\) prior \(\times\) likelihood) via the convolution of the two Gaussian distributions. In the EAKF, variables and parameters are updated deterministically such that the higher moments of the prior distribution are preserved in the posterior. Details on the implementation of the EAKF can be found in published studies [ 27 , 42 ].

In the analysis, we first focus on the period before lockdown and stay-at-home order were announced in each city. For Wuhan, New York, Milan and London, we used daily case data reported from January 16 to January 23, March 1 to March 19, February 25 to March 8, and March 6 to March 23, respectively. We collected the daily reported case data in the four cities (see details in Availability of data and materials). To mitigate the impact of possible irregular reporting during the early stage of the pandemic, we used a five-day moving average to smooth the epidemic curves. Before lockdown, confirmed cases were isolated but population-level stay-at-home orders were not yet affected. We therefore set the self-isolation rate \(q_0\) as zero. To reduce the number of unknown parameters, we fixed several parameters estimated in previous studies. Specifically, the isolation rate of confirmed cases \(q_3\) is estimated as the reciprocal of the delay from the onset of contagiousness to confirmation. For instance, the confirmation delay in Wuhan was estimated to be 6-10 days [ 2 , 43 ]; we therefore set the range of \(q_3\) to be [0.1, 0.16] for Wuhan. Confirmation delays in New York, Milan and London were also reported in previous studies [ 21 , 43 , 44 , 45 ]. We used these estimates to define \(q_3\) in these cities. Certain exposed individuals and unreported infections may be quarantined before the lockdown. We fixed the range of quarantine rates \(q_1\) and \(q_2\) as [0.05, 0.1] in the model. We further fixed relative transmission rate \(\mu\) , the mean latency period L and mean infectious period D as reported in other studies [ 2 ]. In model simulations and inference before lockdown, these parameters were uniformly drawn from the prior range and were fixed throughout the analysis. We used the model-inference framework to estimate two parameters: \(\alpha\) , \(\beta\) . The fixed ranges of \(q_1\) , \(q_2\) , \(q_3\) , \(\mu\) , L and D and the prior ranges of \(\alpha\) , \(\beta\) were shown in Table 1 . We used 300 ensemble members in the EAKF, and drew initial parameters uniformly from the prior ranges.

Counterfactual simulations after lockdown

We estimated the number of COVID-19 cases averted by stay-at-home and quarantine measures after lockdown using counterfactual simulations. We first employed the model-inference system to estimate daily posterior model parameters within 40 days after lockdown in each city. Specifically, we estimated the daily transmission rate \(\beta\) , ascertainment rate \(\alpha\) , self-isolation rate \(q_0\) , quarantine rates \(q_1\) and \(q_2\) , isolation rate of confirmed cases \(q_3\) , relative transmissibility of undocumented infections \(\mu\) , mean latency period L , and mean infectious duration D . We further fixed the average length of self-isolation Q as 75 (from January 24 to April 7), 80 (from March 20 to June 7), 56 (from March 9 to May 3) and 48 (from March 24 to May 10) days for Wuhan, New York, Milan and London, respectively. The parameter inference was performed for 100 realizations independently, each with different initialization of the ensemble members in the EAKF, to obtain parameter combinations that fit the observed case data. Note, we estimated the time-varying parameters after lockdown to reflect changing control measures and testing practice.

We then plugged in the estimated daily parameters and ran model simulations for 40 days, for which we varied the self-isolation rate \(q_0\) and quarantine rates \(q_1\) and \(q_2\) . In counterfactual simulations, we tested the following two scenarios. In the first, we set \(q_0=q_1=q_2=0\) after lockdown, assuming no stay-at-home orders and quarantine were implemented. In this analysis, we focus on the combined effect of stay-at-home orders and quarantine. In the second, we only set \(q_0=0\) and keep \(q_1\) and \(q_2\) unchanged as the estimated values. This counterfactual simulation estimates the effect of stay-at-home orders. The counterfactual simulations can inform the number of COVID-19 cases averted by stay-at-home orders and quarantine measures. Note in counterfactual simulations, we only lifted stay-at-home and quarantine for susceptible, exposed, and unreported infections. Other model parameters, such as the time-varying transmission rate and ascertainment rate, remain the same as estimated in the model.

Epidemiological characteristics before lockdown

Using the model-data assimilation framework, we estimated the posterior epidemiological parameters in the four cities before lockdown (see Table 2 ). The posterior fitting agrees well with observed daily cases, as shown in Figure 2 . Before lockdown, the number of new cases increased rapidly during the study period, and most data points fall within the 95% CI. The estimated effective reproductive number \(R_e\) is generally in line with previous estimates [ 5 ]. New York has the highest estimated \(R_e=2.89\) , followed by London ( \(R_e=2.80\) ), Milan ( \(R_e=2.70\) ) and Wuhan ( \(R_e=2.25\) ). Only \(8.6\%\) infections were estimated to be confirmed in New York, agreeing with previous modeling results [ 35 ] and surveys of healthcare-seeking behavior [ 46 ] and seroprevalence [ 47 ]. For Wuhan, a seroprevalence study found that the ascertainment rate before April 2020 was about 6.8% [ 48 ], which is close to our estimate of 7.4%. We also estimated the ascertainment rates in Milan and London to be 7.4% and 7.6%, respectively. Serological surveys in Milan [ 49 ] and London [ 50 ] resulted in 7.1% and 7.1% ascertainment rates, generally matching our estimates.

Impact of stay-at-home and quarantine rates on COVID-19 spread

We use model simulations to examine the minimal self-isolation rate \(q_0\) and quarantine rates \(q_1\) and \(q_2\) required to reduce \(R_e\) below one. Estimates of these threshold values are important from a public health point of view. First, these two rates can be changed by the compliance with policies. If necessary, local governments can enforce stricter policies and adopt more effective contact tracing to increase the stay-at-home and quarantine rates to reduce \(R_e\) below one. Second, in settings where these two rates cannot be modified, the threshold can indicate whether it is possible to contain the outbreak through stay-at-home and quarantine alone. The threshold values can be used to assess the controllability of the disease.

We initialized the transmission model using model states and parameters inferred on the last day before lockdown (as shown in Table 2 ), varied self-isolation rate \(q_0\) (caused by stay-at-home orders) and the quarantine rates \(q_1\) (the quarantine rate of exposed individuals) and \(q_2\) (the quarantine rate of unreported infections), and ran model simulations until the outbreak stops. We examined the effects of different combinations of self-isolation and quarantine rates on the effective reproductive number, the duration of outbreak, the attack rate, and peak timing. For the ease of visualization in 2D plots, we fixed one parameter and varied the other two in model simulations. The choice of the fixed parameter values is arbitrary and does not impact the qualitative results.

Figure 3 shows the impact of quarantine rates \(q_1\) and \(q_2\) on the effective reproductive number \(R_e\) on the first day of model simulation for fixed self-isolation rates \(q_0=0\) (solid lines) and \(q_0=0.1\) (dash lines). The figure shows the combinations of \(q_1\) and \(q_2\) that lead to \(R_e=1\) in the four cities. The results were obtained for \(q_3\) set as in Table 1 , i.e. the same isolation rate of confirmed cases as before lockdown. If susceptible individuals are not self-isolated (i.e., no stay-at-home orders) and control measures on confirmed cases remain the same after lockdown, quarantining only unreported infections is not sufficient to contain the outbreak in New York and London - even with \(q_2=1\) , the effective reproductive number \(R_e\) is still above one. For Wuhan and Milan, it is possible to reduce \(R_e\) below one through the quarantine of only undetected infections, but the majority of undocumented cases need to be quarantined quickly ( \(q_2\) is close to 1). In reality, this is very challenging because rapid testing is not widely available and the turnaround time of PCR testing is too long to support timely quarantine. As a result, it is necessary to self-isolate susceptible population in order to control the outbreak. Model simulations also indicate that, for \(q_0=0.1\) , self-isolation of susceptible population can substantially reduce the required quarantine rates \(q_1\) and \(q_2\) for \(R_e<1\) , suggesting that stay-at-home orders are more effective in reducing effective reproductive numbers.

We performed similar analyses by fixing \(q_1\) (Fig. 4 ) and \(q_2\) (Fig. 5 ). In order to reduce \(R_e\) below 1, undocumented infections or exposed individuals need to be quarantined with a much faster quarantine rate than the self-isolation of susceptible population. This pattern consistently holds across all four cities. The required self-isolation rate of susceptible population \(q_0\) decreases with increased quarantine rate of undocumented infection \(q_2\) and exposed individual \(q_1\) . In order to minimize the population size under self-isolation and reduce the disturbance on society, the best control strategy should be to isolate confirmed cases and individuals who are exposed to infections (possible undocumented infections) as soon as possible so that the required self-isolation rate of susceptible population could be lower.

Dynamics of the transmission model. The compartments S , E , \(I_r\) , \(I_u\) and R represent susceptible, exposed, reported infected, unreported infected and removed populations. \(S_q\) , \(E_q\) and \(I_q\) are susceptible, exposed and infected individuals under quarantine. \(q_0\) is the self-isolation rate of susceptible persons; \(q_1\) is the quarantine rate of exposed persons; \(q_2\) is the quarantine rate of unreported infections; and \(q_3\) is the isolation rate of confirmed cases. \(\beta\) is the transmission rate of SARS-CoV-2, L is the mean duration of latency period, D is the mean duration of infectious period, and Q is the average length of self-isolation

Model fitting for a Wuhan from January 16 to January 23, ( b ) New York from March 1 to March 19, c Milan from February 25 to March 8, and d London from March 6 to March 23. The orange star symbol represents reported case number, the blue curve is the mean posterior fitting using the EAKF, and the gray region shows the 95% CI

We further explore the impact of self-isolation rate \(q_0\) and quarantine rates \(q_1\) and \(q_2\) on several characteristics of the outbreak, including the outbreak duration, attack rate, and peak timing. Here the outbreak duration is defined as the number of days it takes for daily cases to drop below 5 after the lockdown measures are enforced; the attack rate is the percentage of population infected with SARS-CoV-2 by the time daily cases fall below 5; and peak timing is the number of days between lockdown and the day with the highest reported daily case. We ran model simulations using different combinations of self-isolation rate \(q_0\) and quarantine rates \(q_1\) and \(q_2\) starting from lockdown with other parameters set as in Tables 1 , 2 , until the daily case number falls to 5.

The combination of \(q_1\) and \(q_2\) that lead to \(R_e=1\) in Wuhan, New York, Milan, and London. The solid lines represent results obtained for \(q_0=0\) , while the dash lines are the results for \(q_0=0.1\) . Other parameters are set as in Tables 1 , 2

The combination of \(q_0\) and \(q_2\) that lead to \(R_e=1\) in Wuhan, New York, Milan, and London. The solid lines represent results obtained for \(q_1=0.1\) , while the dash lines are the results for \(q_1=0.3\) . Other parameters are set as in Tables 1 , 2

We first fixed the self-isolation rate \(q_0\) at 0.03 and varied quarantine rates \(q_1\) and \(q_2\) . Simulation results are shown in Fig. 6 . The outbreak duration is maximized for the combinations of \(q_1\) and \(q_2\) that lead to \(R_e=1\) . For \(R_e>1\) , the outbreak depletes susceptible population and stops due to herd immunity; for \(R_e<1\) , the outbreak dies out as the low secondary infection rate cannot support self-sustained transmission, leaving the majority of population susceptible. At the critical state \(R_e=1\) , the outbreak would linger for a long period until herd immunity stops disease spread. Peak timing also follows the same pattern, as shown in the right column of Fig. 6 . The outbreak duration and peak timing is shorter in cities with higher \(R_e\) before lockdown. Attack rate increases with lower rates \(q_1\) and \(q_2\) . Without control, over 20% population in Wuhan would be infected, while 50% population in the other three cities. We repeated the same analysis for fixed \(q_1=0.1\) (Fig. 7 ) and \(q_2=0.1\) (Fig. 8 ).

The combination of \(q_0\) and \(q_1\) that lead to \(R_e=1\) in Wuhan, New York, Milan, and London. The solid lines represent results obtained for \(q_2=0.1\) , while the dash lines are the results for \(q_2=0.3\) . Other parameters are set as in Tables 1 , 2

Impact of quarantine rates \(q_1\) and \(q_2\) on the outbreak duration, attack rate, and peak timing. The self-isolation rate \(q_0\) is set as 0.03. The first column shows the duration of the outbreak, i.e., the number of days it takes for daily cases to drop below 5 after the lockdown measures are enforced; the second column shows the attack rate by the time when daily cases fall below 5; the third column shows peak timing of daily cases after lockdown, defined as the number of days between lockdown and the day with the highest reported daily case. Each row corresponds to one city

Estimating the averted cases due to quarantine measures

We ran counterfactual simulations starting from the date of lockdown in each city assuming no stay-at-home and quarantine measures were implemented ( \(q_0=q_1=q_2=0\) ). We compare the counterfactual simulation outcomes with observed cases numbers in Fig. 9 . Without quarantine measures, the outbreak would get out of control and result in massive disease spread. In total, we estimated that the quarantine measures have averted 3,589,622, 3,281,480, 629,046 and 2,452,750 confirmed cases in Wuhan, New York, Milan, and London during the 40-day period after lockdown. In other words, the cumulative case number would be 71, 21, 41 and 99 times higher than the reported number during this period in Wuhan, New York, Milan, and London. These counterfactual simulations indicate that strict stay-at-home and quarantine measures are essential to control the spread of COVID-19 during the early phase of the pandemic.

Impact of self-isolation rate \(q_0\) and quarantine rate \(q_2\) on the outbreak duration, attack rate, and peak timing. The quarantine rate \(q_1\) is set as 0.1. The first column shows the duration of the outbreak, i.e., the number of days it takes for daily cases to drop below 5 after the lockdown measures are enforced; the second column shows the attack rate by the time when daily cases fall below 5; the third column shows peak timing of daily cases after lockdown, defined as the number of days between lockdown and the day with the highest reported daily case. Each row corresponds to one city

Impact of self-isolation rate \(q_0\) and quarantine rate \(q_1\) on the outbreak duration, attack rate, and peak timing. The quarantine rate \(q_2\) is set as 0.1. The first column shows the duration of the outbreak, i.e., the number of days it takes for daily cases to drop below 5 after the lockdown measures are enforced; the second column shows the attack rate by the time when daily cases fall below 5; the third column shows peak timing of daily cases after lockdown, defined as the number of days between lockdown and the day with the highest reported daily case. Each row corresponds to one city

In addition, we ran counterfactual simulations in which self-isolation of the susceptible population is not enacted, that is, \(q_0=0\) , and other parameters remain unchanged as estimated. The results showed that, compared with the counterfactual scenario that quarantine is in place but stay-at-home is not effected, 570,696 confirmed cases were averted by self-isolation in Wuhan, 283,020 in New York, 21,255 in Milan, and 81,737 in London. Compared with the results in Fig. 9 , the averted cases are much lower. This indicates that stay-at-home orders need to work in synergy with quarantine to effectively limit COVID-19 spread.

Counterfactual simulations of outbreaks in Wuhan, New York, Milan and London assuming no stay-at-home and quarantine measures. Simulations were performed using posterior model parameters estimated each day in the four cities within 40 days after lockdown, with the self-isolation rate \(q_0=0\) and quarantine rates \(q_1=q_2=0\) . The vertical dash lines show the starting dates of counterfactual simulations. The orange stars are the observed daily case numbers. The blue curve is the mean posterior fitting using the EAKF. Blue boxes show the median and interquartile of counterfactual simulations, and whiskers show 95% CI. The solid red lines are the median of counterfactual simulations. Counterfactual simulations were performed for 100 realizations using independently estimated model parameters

In this study, we developed an SEIR-type disease transmission model to evaluate the impact of stay-at-home and quarantine measures on COVID-19 spread in four cities that experienced early large-scale outbreaks - Wuhan, New York, Milan and London. Using the transmission model in conjunction with data assimilation techniques, we estimated key epidemiological parameters in each city before lockdown. We examined the impact of stay-at-home and quarantine on COVID-19 spread after lockdown by adjusting self-isolation and quarantine rates in counterfactual simulations. We found that quarantine of susceptible population is necessary to contain the outbreak. Self-isolation of susceptible population induced by stay-at-home orders is more effective in reducing effective reproductive numbers \(R_e\) . Variation in self-isolation and quarantine rates can also considerably affect the duration of outbreaks, attack rates and peak timing. We generate counterfactual simulations to estimate effectiveness of stay-at-home and quarantine measures. Without these two measures, the cumulative confirmed cases could be much higher than reported numbers within 40 days after lockdown in Wuhan, New York, Milan, and London.

There are several limitations in the study. First, we neglected the effect of quarantine of SARS-CoV-2 contacts on the susceptible population. In reality, the number of exposed susceptible individuals may increase with the number of infectious individuals. However, as only a small fraction of susceptible individuals have close contacts with infectious persons during the early pandemic, we believe this model simplification does not significantly affect our results. Second, we did not explicitly consider contact tracing efforts implemented after lockdown. The contact tracing capacity was limited at the beginning of the pandemic, and the effect of contact tracing can be implicitly represented by elevated ascertainment rate. Thirdly, we assume model parameters in counterfactual simulations such as the transmission rate and ascertainment rate remain the same as estimated using real-world data. However, human behavior may change in response to large-scale local outbreaks. Our counterfactual results are therefore conditioned on the idealized assumption that population behavior does not change except self-isolation and quarantine rates. Lastly, human behaviors and cultures vary in different counties and could impact the compliance with control measures. In this study, we focused on four metropolitan areas in developed settings. Results in developing countries may be different given potential differing behaviors and cultures.

Availability of data and materials

The data of Wuhan is collected from the daily news published on the official website of the Hubei Provincial Health Commission: http://wjw.hubei.gov.cn . The New York City data is published by THE CITY at https://github.com/thecityny/covid-19-nyc-data , and their official website is https://thecity.nyc . The data for Milan is available at https://github.com/RamiKrispin/covid19Italy by Rami Krispin. The data for London is collected from https://coronavirus.data.gov.uk . All data are publicly available. The code is uploaded as the Additional file 1 and can be freely downloaded.

Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533–4. https://doi.org/10.1016/S1473-3099(20)30120-1 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, Shaman J. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (sars-cov-2). Science. 2020;368(6490):489–93. https://doi.org/10.1126/science.abb3221 .

Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PG, Fu H, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis. 2020;20(6):669–77. https://doi.org/10.1016/S1473-3099(20)30243-7 .

Xu X-K, Liu XF, Wu Y, Ali ST, Du Z, Bosetti P, Lau EH, Cowling BJ, Wang L. Reconstruction of transmission pairs for novel coronavirus disease 2019 (COVID-19) in Mainland China: estimation of superspreading events, serial interval, and hazard of infection. Clin Infect Dis. 2020;71(12):3163–7. https://doi.org/10.1093/cid/ciaa790 .

Zhao S, Lin Q, Ran J, Musa SS, Yang G, Wang W, Lou Y, Gao D, Yang L, He D, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-ncov) in China, from 2019 to 2020: a data-driven analysis in the early phase of the outbreak. Int J Infect Dis. 2020;92:214–7. https://doi.org/10.1016/j.ijid.2020.01.050 .

Kraemer MU, Yang C-H, Gutierrez B, Wu C-H, Klein B, Pigott DM, Du Plessis L, Faria NR, Li R, Hanage WP, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 2020;368(6490):493–7. https://doi.org/10.1126/science.abb4218 .

Article   CAS   PubMed   Google Scholar  

Zhang J, Klepac P, Read JM, Rosello A, Wang X, Lai S, Li M, Song Y, Wei Q, Jiang H, et al. Patterns of human social contact and contact with animals in Shanghai, China. Sci Rep. 2019;9(1):1–11. https://doi.org/10.1038/s41598-019-51609-8 .

Article   CAS   Google Scholar  

Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, Eggo RM, Sun F, Jit M, Munday JD, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis. 2020;20(5):553–8. https://doi.org/10.1016/S1473-3099(20)30144-4 .

Du Z, Wang L, Cauchemez S, Xu X, Wang X, Cowling BJ, Meyers LA. Risk for transportation of coronavirus disease from wuhan to other cities in China. Emerging infectious diseases. 2020;26(5):1049. https://doi.org/10.3201/eid2605.200146 .

Xu X-K, Liu XF, Wang L, Wu Y, Lu X, Wang X, Pei S. Assessing the spread risk of COVID-19 associated with multi-mode transportation networks in China. Fundamental Research. 2022. https://doi.org/10.1016/j.fmre.2022.04.006 .

Article   PubMed   PubMed Central   Google Scholar  

Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, Whittaker C, Zhu H, Berah T, Eaton JW, et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in europe. Nature. 2020;584(7820):257–61. https://doi.org/10.1038/s41586-020-2405-7 .

Brauner JM, Mindermann S, Sharma M, Johnston D, Salvatier J, Gavenčiak T, Stephenson AB, Leech G, Altman G, Mikulik V, et al. Inferring the effectiveness of government interventions against COVID-19. Science. 2021. https://doi.org/10.1126/science.abd9338 .

Article   PubMed   Google Scholar  

Hsiang S, Allen D, Annan-Phan S, Bell K, Bolliger I, Chong T, Druckenmiller H, Huang LY, Hultgren A, Krasovich E, et al. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature. 2020;584(7820):262–7. https://doi.org/10.1038/s41586-020-2404-8 .

Ferguson N, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, Bhatia S, Boonyasiri A, Cucunubá Z, Cuomo-Dannenburg G, et al. Report 9: impact of non-pharmaceutical interventions (npis) to reduce COVID19 mortality and healthcare demand. Imperial Coll Lond. 2020;10(77482):491–7. https://doi.org/10.25561/77482 .

Article   Google Scholar  

Ali ST, Wang L, Lau EH, Xu X-K, Du Z, Wu Y, Leung GM, Cowling BJ. Serial interval of sars-cov-2 was shortened over time by nonpharmaceutical interventions. Science. 2020;369(6507):1106–9. https://doi.org/10.1126/science.abc9004 .

Xu X-K, Wang L, Pei S. Multiscale mobility explains differential associations between the gross domestic product and covid-19 transmission in chinese cities. J Travel Med. 2021;28(2):236. https://doi.org/10.1093/jtm/taaa236 .

Galanti M, Pei S, Yamana TK, Angulo FJ, Charos A, Khan F, Shea KM, Swerdlow DL, Shaman J. Non-pharmaceutical interventions and inoculation rate shape sars-cov-2 vaccination campaign success. Epidemiol Infect. 2021. https://doi.org/10.1017/S095026882100217X .

Galanti M, Pei S, Yamana TK, Angulo FJ, Charos A, Swerdlow DL, Shaman J. Social distancing remains key during vaccinations. Science. 2021;371(6528):473–4. https://doi.org/10.1126/science.abg2326 .

Fraser C, Riley S, Anderson RM, Ferguson NM. Factors that make an infectious disease outbreak controllable. Proc Natl Acad Sci USA. 2004;101(16):6146–51. https://doi.org/10.1073/pnas.0307506101 .

Peak CM, Childs LM, Grad YH, Buckee CO. Comparing nonpharmaceutical interventions for containing emerging epidemics. Proc Natl Acad Sci USA. 2017;114(15):4023–8. https://doi.org/10.1073/pnas.1616438114 .

Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, Munday JD, Kucharski AJ, Edmunds WJ, Sun F, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health. 2020;8(4):488–96. https://doi.org/10.1016/S2214-109X(20)30074-7 .

Grasselli G, Pesenti A, Cecconi M. Critical care utilization for the COVID-19 outbreak in lombardy, italy: early experience and forecast during an emergency response. JAMA. 2020;323(16):1545–6. https://doi.org/10.1001/jama.2020.4031 .

Sayampanathan AA, Heng CS, Pin PH, Pang J, Leong TY, Lee VJ. Infectivity of asymptomatic versus symptomatic covid-19. Lancet. 2021;397(10269):93–4. https://doi.org/10.1016/S0140-6736(20)32651-9 .

Bi Q, Lessler J, Eckerle I, Lauer SA, Kaiser L, Vuilleumier N, Cummings DA, Flahault A, Petrovic D, Guessous I, et al. Household transmission of sars-cov-2: insights from a population-based serological survey. MedRxiv. 2021. https://doi.org/10.1101/2020.11.04.20225573 .

Gao M, Yang L, Chen X, Deng Y, Yang S, Xu H, Chen Z, Gao X. A study on infectivity of asymptomatic sars-cov-2 carriers. Respir Med. 2020;169: 106026. https://doi.org/10.1016/j.rmed.2020.106026 .

Byambasuren O, Cardona M, Bell K, Clark J, McLaws M-L, Glasziou P. Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis. Off J Assoc Med Microbiol Infect Dis Canada. 2020;5(4):223–34. https://doi.org/10.3138/jammi-2020-0030 .

Anderson JL. An ensemble adjustment kalman filter for data assimilation. Mon Weather Rev. 2001;129(12):2884–903. https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2

Navon IM. Data assimilation for numerical weather prediction: a review. Data Assimil Atmos Ocean Hydrol Appl. 2009; 21–65. https://doi.org/10.1007/978-3-540-71056-1_2

Pei S, Kandula S, Yang W, Shaman J. Forecasting the spatial transmission of influenza in the united states. Proc Natl Acad Sci USA. 2018;115(11):2752–7. https://doi.org/10.1073/pnas.1708856115 .

Kandula S, Yamana T, Pei S, Yang W, Morita H, Shaman J. Evaluation of mechanistic and statistical methods in forecasting influenza-like illness. J R Soc Interface. 2018;15(144):20180174. https://doi.org/10.1098/rsif.2018.0174 .

Pei S, Shaman J. Counteracting structural errors in ensemble forecast of influenza outbreaks. Nat Commun. 2017;8(1):1–10. https://doi.org/10.1038/s41467-017-01033-1 .

Kramer SC, Pei S, Shaman J. Forecasting influenza in europe using a metapopulation model incorporating cross-border commuting and air travel. PLoS Comput Biol. 2020;16(10):1008233. https://doi.org/10.1371/journal.pcbi.1008233 .

Kandula S, Pei S, Shaman J. Improved forecasts of influenza-associated hospitalization rates with google search trends. J R Soc Interface. 2019;16(155):20190080. https://doi.org/10.1098/rsif.2019.0080 .

Pei S, Yamana TK, Kandula S, Galanti M, Shaman J. Burden and characteristics of COVID-19 in the United States during 2020. Nature. 2021. https://doi.org/10.1038/s41586-021-03914-4 .

Pei S, Shaman J. Initial simulation of sars-cov2 spread and intervention effects in the continental us. medRxiv. 2020. https://doi.org/10.1101/2020.03.21.20040303 .

Ma Y, Pei S, Shaman J, Dubrow R, Chen K. Role of meteorological factors in the transmission of sars-cov-2 in the United States. Nat Commun. 2021;12(1):1–9. https://doi.org/10.1038/s41467-021-23866-7 .

Yamana T, Pei S, Kandula S, Shaman J. Projection of COVID-19 cases and deaths in the us as individual states re-open may 4, 2020. MedRxiv. 2020. https://doi.org/10.1101/2020.05.04.20090670 .

Pei S, Dahl KA, Yamana TK, Licker R, Shaman J. Compound risks of hurricane evacuation amid the COVID-19 pandemic in the United States. GeoHealth. 2020;4(12):2020–000319. https://doi.org/10.1029/2020GH000319 .

Pei S, Teng X, Lewis P, Shaman J. Optimizing respiratory virus surveillance networks using uncertainty propagation. Nat Commun. 2021;12(1):1–10. https://doi.org/10.1038/s41467-020-20399-3 .

Pei S, Morone F, Liljeros F, Makse H, Shaman JL. Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus . eLife. 2018;7:40977. https://doi.org/10.7554/eLife.40977.001 .

Pei S, Liljeros F, Shaman J. Identifying asymptomatic spreaders of antimicrobial-resistant pathogens in hospital settings. Proc Natl Acad Sci. 2021. https://doi.org/10.1073/pnas.2111190118 .

Yang W, Karspeck A, Shaman J. Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics. PLoS Comput Biol. 2014;10(4):1003583. https://doi.org/10.1371/journal.pcbi.1003583 .

Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KS, Lau EH, Wong JY, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med. 2020. https://doi.org/10.1056/NEJMoa2001316 .

Pei S, Kandula S, Shaman J. Differential effects of intervention timing on COVID-19 spread in the United States. Sci Adv. 2020;6(49):6370. https://doi.org/10.1126/sciadv.abd6370 .

Riccardo F, Ajelli M, Andrianou XD, Bella A, Del Manso M, Fabiani M, Bellino S, Boros S, Urdiales AM, Marziano V, et al. Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020. Eurosurveillance. 2020;25(49):2000790. https://doi.org/10.2807/1560-7917.ES.2020.25.49.2000790 .

Article   CAS   PubMed Central   Google Scholar  

Reese H, Iuliano AD, Patel NN, Garg S, Kim L, Silk BJ, Hall AJ, Fry A, Reed C. Estimated incidence of coronavirus disease 2019 (COVID-19) illness and hospitalization-United States, February-September 2020. Clin Infect Dis. 2021;72(12):1010–7. https://doi.org/10.1093/cid/ciaa1780 .

Stadlbauer D, Tan J, Jiang K, Hernandez MM, Fabre S, Amanat F, Teo C, Arunkumar GA, McMahon M, Capuano C, et al. Repeated cross-sectional sero-monitoring of sars-cov-2 in New York City. Nature. 2021;590(7844):146–50. https://doi.org/10.1038/s41586-020-2912-6 .

He Z, Ren L, Yang J, Guo L, Feng L, Ma C, Wang X, Leng Z, Tong X, Zhou W, et al. Seroprevalence and humoral immune durability of anti-sars-cov-2 antibodies in Wuhan, China: a longitudinal, population-level, cross-sectional study. Lancet. 2021;397(10279):1075–84. https://doi.org/10.1016/S0140-6736(21)00238-5 .

Valenti L, Bergna A, Pelusi S, Facciotti F, Lai A, Tarkowski M, Berzuini A, Caprioli F, Santoro L, Baselli G, et al. Sars-cov-2 seroprevalence trends in healthy blood donors during the COVID-19 Milan outbreak. MedRxiv. 2020. https://doi.org/10.1101/2020.05.11.20098442 .

Arora RK, Joseph A, Van Wyk J, Rocco S, Atmaja A, May E, Yan T, Bobrovitz N, Chevrier J, Cheng MP, et al. Serotracker: a global sars-cov-2 seroprevalence dashboard. Lancet Infect Dis. 2021;21(4):75–6. https://doi.org/10.1016/S1473-3099(20)30631-9 .

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R.Z. is supported by National Natural Science Foundation of China (Grant No.11801058), High-level Talents Program of Dalian City (Grant No.2020RQ061), National Key Research and Development Program of China (Grant No.2020YFA0713702), Provincial College Student Innovation and Entrepreneurship Training Program Support Project (Grant No.20211014110119) and the Fundamental Research Funds for the Central Universities (DUT20LK41, DUT20YG125). S.P. is supported by US CDC Grant 20U01CK000592 and CSTE Grant NU38OT00297.

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Renquan Zhang & Yu Wang

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Zhang, R., Wang, Y., Lv, Z. et al. Evaluating the impact of stay-at-home and quarantine measures on COVID-19 spread. BMC Infect Dis 22 , 648 (2022). https://doi.org/10.1186/s12879-022-07636-4

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Treating COVID-19 at home: Care tips for you and others

Providing care at home for a person sick with COVID-19? Or caring for yourself at home? Understand when emergency care is needed and what you can do to prevent the spread of infection.

If you have COVID-19, also called coronavirus disease 2019, you may have some questions. COVID-19 can affect people differently. Whether you're caring for yourself or someone else at home, here is some basic information on emergency care, how to stop the spread of the COVID-19 virus and when you can get back to being with others.

At-home care for COVID-19

Many people with COVID-19 get better with rest, fluids and treatment for their symptoms. Medicine you can get without a prescription can help.

Some examples are:

• Fever reducers.
• Pain relievers, such as ibuprofen (Advil, Motrin IB, others) or acetaminophen (Tylenol, others).
• Cough syrup or medicine.

A person at high risk of serious COVID-19 illness may be offered medicine to prevent mild illness from getting worse.

Groups at higher risk are people age 65 and older, babies younger than 6 months, and people with certain medical conditions. Those conditions include blood disorders and chronic diseases.

If you are looking after someone with COVID-19, help the person track symptoms. You may need to help with child care or getting food and any medicine needed. And it can help to take care of the person's pet.

For as long as COVID-19 symptoms get worse, stay home and apart from people who don't have COVID-19. That will help stop the spread of the virus. People with weakened immune systems may need to stay apart, also called isolate, for longer. Your healthcare professional can advise you on what's best in your situation.

If you have COVID-19 and are staying separate from others, it can be stressful. You can take these actions to help your body and mind through the illness and isolation:

• Eat healthy foods.
• Get the rest you need.
• Try relaxation exercises.
• Keep up with hobbies you enjoy.
• Connect with others through phone or video calls.

Also, if you're caring for someone with COVID-19, think about how it might affect your health. If you are age 65 or older or have chronic medical conditions, you may be at higher risk of serious illness with COVID-19.

Your best protection is a recent COVID-19 vaccine. But you might think about staying apart from the person with COVID-19. If other people could provide care, that might help lower your risk. Other actions, such as increasing airflow in your living space and wearing a face mask, can help you avoid getting the virus that causes COVID-19.

Emergency warning signs of COVID-19

Carefully watch yourself or the person you're caring for to see if COVID-19 symptoms are getting worse.

Get emergency help right away for any of these symptoms:

• Breathing problems or not being able to catch your breath.
• Skin, lips or nail beds that are gray or blue.
• New confusion.
• Trouble staying awake or waking up.
• Chest pain or pressure that is constant.

This list doesn't include all symptoms. If you or a person you're taking care of has symptoms that worry you, get help. Let the healthcare team know about a positive test for COVID-19 or symptoms of the illness.

Protecting others if you have COVID-19

To prevent the spread of the COVID-19 virus to others, stay home and apart from anyone you live with for as long as you have worsening symptoms. You can wear a face mask if you must be around other people.

You also can take other actions that lower the chance of spreading the virus that causes COVID-19:

• Wash your hands well and often using soap and water for at least 20 seconds.
• Cover your coughs and sneezes.
• Clean and disinfect surfaces you touch often.
• Do not share towels, cups or other items if possible.
• Use a separate bathroom and bedroom if possible.
• Get more airflow in your home.

Once you're feeling better and haven't had a fever for a full 24 hours without taking medicine for fever, you can go back to being around others. If your fever comes back or you start to feel worse, return to isolation until your symptoms improve and you are fever-free without fever-reducing medicine for 24 hours. But listen to the advice of your healthcare professional.

In the five days after isolation, to help prevent the spread of the COVID-19 virus, you can wear a mask and keep up with the actions that prevent the coronavirus from spreading. These actions are helpful even if you never had symptoms but tested positive for COVID-19.

Protecting yourself while caring for someone with COVID-19

As you care for someone with COVID-19, avoid touching that person's fluids. Wash your hands after cleaning up waste such as used tissues, vomit, stool or urine.

Continue all the actions that help prevent the spread of the virus that causes COVID-19. This includes washing your hands with soap and water for at least 20 seconds, not touching your face, wearing a face mask when you're in the same room as the person who is ill, and cleaning the home. But avoid cleaning the room where the person is isolating and set aside bedding, towels and utensils for the sick person only to use.

Avoid direct physical contact with the person who has COVID-19. Also try to limit visitors until the person has recovered.

After recovery from COVID-19

As you or the person you're caring for gets better, watch for any symptoms that don't go away. Some people report symptoms that continue for months or new medical issues after having COVID-19. Make sure to track symptoms and contact your healthcare professional if they don't get better.

Also, once you recover, you will likely have some protection from getting the virus that causes COVID-19 again. But that protection seems to fade over time. Getting a COVID-19 vaccine can boost your body's protection and help prevent you from getting the virus again.

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• Coronavirus disease 2019 (COVID-19) treatment guidelines. National Institutes of Health. https://www.covid19treatmentguidelines.nih.gov/. Accessed March 27, 2024.
• Interim clinical considerations for COVID-19 treatment in outpatients. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/outpatient-treatment-overview.html. Accessed March 27, 2024.
• Know your treatment options for COVID-19. U.S. Food and Drug Administration. https://www.fda.gov/consumers/consumer-updates/know-your-treatment-options-covid-19. Accessed March 27, 2024.
• Regan JJ, et al. Use of updated COVID-19 vaccines 2023-2024 formula for persons aged ≥6 months: Recommendations of the Advisory Committee on Immunization Practices — United States, September 2023. MMWR Morbidity and Mortality Weekly Report 2023; doi:10.15585/mmwr.mm7242e1.
• Goldman L, et al., eds. COVID-19: Epidemiology, clinical manifestations, diagnosis, community prevention, and prognosis. In: Goldman-Cecil Medicine. 27th ed. Elsevier; 2024. https://www.clinicalkey.com. Accessed Dec. 17, 2023.
• People with certain medical conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html. Accessed March 27, 2024.
• Preventing spread of respiratory viruses when you're sick. Centers for Disease Control and Prevention. https://www.cdc.gov/respiratory-viruses/prevention/precautions-when-sick.html. Accessed March 27, 2024.
• AskMayoExpert. COVID-19: Quarantine and isolation. Mayo Clinic. 2024.
• COVID-19 resource and information guide. National Alliance on Mental Illness. https://www.nami.org/Support-Education/NAMI-HelpLine/COVID-19-Information-and-Resources/COVID-19-Resource-and-Information-Guide. Accessed March 27, 2024.
• What to do if you have COVID-19. Centers for Disease Control and Prevention. https:// www.cdc.gov/coronavirus/2019-ncov/if-you-are-sick/steps-when-sick.html. Accessed March 27, 2024.
• Symptoms of COVID-19. Centers for Disease Control and Preventions. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html. Accessed March 27, 2024.
• How to protect yourself and others. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html. Accessed March 27, 2024.
• COVID-19 overview and infection prevention and control priorities in non-U.S. healthcare settings. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/hcp/non-us-settings/overview/index.html. Accessed March 27, 2024.
• Long COVID or post-COVID conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html. Accessed March 27, 2024.
• Getting your COVID-19 vaccine. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/expect.html. Accessed March 27, 2024.
• DeSimone DC (expert opinion). Mayo Clinic. March 27, 2024.

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Masks Strongly Recommended but Not Required in Maryland, Starting Immediately

Due to the downward trend in respiratory viruses in Maryland, masking is no longer required but remains strongly recommended in Johns Hopkins Medicine clinical locations in Maryland. Read more .

• Vaccines  
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Staying at Home During COVID-19: How to Help Teens Cope

Featured Expert:

Nilu Rahman, M.S., C.C.L.S.

Just when teenagers were looking forward to spring and summer, the COVID-19 pandemic has postponed or canceled events and limited a lot of their favorite activities, including parties, sports, and time spent in person with friends. No wonder many teenagers are feeling depressed, angry and bored.

These responses are normal, according to Johns Hopkins Children’s Center senior child life specialist Nilu Rahman , who offers suggestions on how parents can help their teens deal with the disappointment of cancellations and postponements and make the most out of their time at home.

Teens Missing Out on Milestones

The COVID-19 pandemic has robbed many graduating high school seniors and other teens of significant rites of passage, such as graduations, “senior week” activities, summer jobs, trips and celebrations.

“Teenagers are grieving,” Rahman says. “They’ve been working hard and looking forward to these events for years, and now they don’t get to attend a prom or walk across the stage for their diplomas.”

According to Rahman, some of these losses are things parents can’t fix. Well-meaning parents may try to help provide some kind of substitute, but their good intentions don’t always pan out. “One mom I know tried to put on a prom for her kid and it sort of backfired, and made the loss feel worse,” Rahman says.

As an alternative, she suggests teenagers look toward the post-pandemic future, and work on a vision of something that will be memorable and fun.

“We're asking teens, ‘When you're finally able to celebrate, what would you want it to look like?’ We’re encouraging them to create collages, vision boards and written plans so they have something they can look forward to, even if it's different from what they originally pictured.”

How Parents Can Help Teens Stuck at Home

“Teens cut off from their normal activities and stuck at home want to feel like they have purpose and meaning,” says Rahman.

Here are some tips to make teens’ stay-at-home days count:

Support new structures

Rahman says some structure can make stay-at-home days more meaningful for teens.

“Don’t just let them flow aimlessly from one hour to the next,” she suggests. “Give them a strategy and help them get everything they can out of their days.” A schedule might include time outside, exercise and participation in social connections while maintaining social distancing, such as a Zoom or FaceTime game night.

Use screen time constructively

Teens love their phones and tablets, and since they’re pretty much a lifeline between teens and their friends, the pandemic may make it difficult to limit screen time.

Rahman says that some social media and online time can be used to launch and complete a project, something with a beginning, middle and end that can give teens a sense of accomplishment.

“Teens can start a book club with friends — read a book together and talk about it,” Rahman says. “They can use social media to try dance challenges, photography projects and other activities, based on their interests.”

Set boundaries and provide purpose

“As a parent, you can impress upon your kids that the pandemic doesn’t mean they can just hang out until further notice,” Rahman says. “Don't be afraid to assign chores and engage teens in the family’s work, such as pitching in to prepare meals.

“And even if you push them to go outside for a walk or a jog, they might grumble at first, but most teens actually appreciate it.”

Discuss the facts about COVID-19 and the pandemic

“Teens have great access to the internet and some of what they’re reading about the coronavirus and the pandemic might be scaring them, even if they don’t say so,” Rahman says. “Parents should make sure kids are not going down rabbit holes and getting confused or frightened by false information.”

She suggests a regular weekly check-in when children and adults can discuss coronavirus information as a family using trustworthy, science-based sources. This can help clear up misunderstandings and give parents a chance to answer teens’ questions honestly and clearly.

Recognize hidden anxieties

Teenagers may act aloof and independent, but behind that facade they might be harboring fears about how COVID-19 might affect them or those they love.

They might be particularly worried about grandparents or parents who have chronic health problems or who work in high-risk professions ranging from health care and other first responders, to grocery and delivery workers. Asking open-ended questions about teens’ concerns may provide them a chance to express their fears.

Teens feel more empowered when they understand that their actions matter. Praising teens for behaviors such as hand-washing, mask-wearing and social distancing shows them that they can play a part in protecting their own health and that of other people around them.

Monitor teens’ mental health

Parents should keep an eye on teens’ mental health, says Rahman. She notes that in her work with teenagers facing chronic illness, fear of the unknown is the toughest part of that experience. She notes that the COVID-19 pandemic has brought a bit of that fear into everyone’s lives.

“Parents know their children best,” she says, “so if something seems off about their teen, they should trust their instinct and find out what’s going on, especially if the child has a history of depression or anxiety.” Specifically, she recommends parents be on the lookout for:

• Sleep changes, such as sleeping more or insomnia
• Eating a lot more or a lot less
• Signs of self-harm or substance use disorder
• Acting out more than usual

When parents note behavioral changes such as these, a call to the family doctor or a mental health practitioner might be appropriate.

“Help is available, and psychologists are working with people of all ages through telehealth visits,” Rahman says.

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Understanding the CDC’s Updated COVID Isolation Guidance

The updated recommendations align guidance for COVID infection with that for other common respiratory viruses.

Aliza Rosen

For the first time since 2021, the Centers for Disease Control and Prevention has updated its COVID isolation guidance.

Specifically, it has shifted the recommendation that someone who tests positive for COVID isolate for five days to a timeline based on the progression of the person’s symptoms. The update is part of a larger strategy to provide one set of recommendations for most common respiratory illnesses , including COVID, influenza, and respiratory syncytial virus (RSV).

In this Q&A, virologist Andy Pekosz , PhD, a professor in Molecular Microbiology and Immunology , explains the CDC’s new isolation guidance, the reasons for the update, and why the prevention and treatment strategies we’ve all become accustomed to still play an important part in reducing respiratory virus transmission.

What are the updated recommendations for someone who comes down with a respiratory infection?

The updated guidance from the CDC is to “stay home and away from others (including people you live with who are not sick) if you have respiratory virus symptoms that aren't better explained by another cause.” You can resume normal activities once your symptoms are improving and you’ve been fever-free—without the aid of fever-reducing medications—for at least 24 hours.

For the five days after you resume your normal activities, you should take extra precautions, like wearing a well-fitting mask and maintaining distance from others, gathering outdoors or in well-ventilated areas, cleaning hands and high-touch surfaces often, and testing when possible before gathering with others. If symptoms or fever return, you should start back at square one: staying home and away from others until you’ve been improving and fever-free for at least 24 hours.

What should you do if you’re at higher risk of severe illness?

If you’re at higher risk of severe illness—generally, this is older adults and young children, pregnant people, people with disabilities, and people with compromised immune systems—seek testing and contact your physician. If you test positive for COVID or flu, there are antiviral medications that can be taken within a few days of symptom onset and are extremely effective in reducing the likelihood that your symptoms become severe or that you need to be hospitalized.

How does this differ from previous guidance?

Before this, the CDC recommended that people who test positive for COVID should isolate away from others for five days and wear a well-fitting mask around others for the following five days. This was different from the general guidance for other common respiratory viruses, like flu and RSV.

Now there is no one-size-fits-all duration for how long to isolate; rather, you can resume regular activities—ideally still using other prevention strategies, like masking and distancing—based on when your symptoms have improved and your fever has gone away. 

This marks a significant change in guidance for people who test positive for COVID. Why has the guidance changed?

The CDC has simplified its recommendations for how long to stay home and isolate after testing positive or experiencing symptoms to be consistent across COVID-19, influenza, and RSV infections. This way, anyone who develops symptoms can follow the same isolation guidance, irrespective of what respiratory virus they’re infected with.

It’s important to note, though, that this guidance on how long to isolate is just one part of a larger strategy for combating respiratory viruses that includes:

• Being up to date on recommended vaccines.
• Practicing good hygiene regarding hand-washing, sneezing, and coughing.
• Being aware of antiviral treatment options for COVID-19 and influenza.
• Taking steps to improve indoor air quality.

If the guidance is the same for all respiratory viruses, is it still important to test to know what someone is sick with?

Yes, testing is still needed in order to get a prescription for antivirals to treat COVID-19 or influenza. Those antivirals have been shown to reduce disease severity in several different groups, so if you are in a high risk group, be sure to test early and contact your physician so you can get the antiviral prescriptions as soon as possible.

Testing can also play an important role in preventing transmission, particularly if you were recently around someone who has since become sick, or if you plan to spend time with someone who is at higher risk of severe infection.

For COVID in particular, rapid home antigen tests are a great way to determine whether you’re still infectious and able to infect others. Symptom severity can be fairly subjective and a presence or lack of symptoms does not always align with infectiousness , so testing out of isolation for COVID is still good practice if you have access to tests.

Does this new guidance mean that all of these respiratory viruses pose the same risk?

No, COVID-19 is still causing more cases and more severe disease than influenza or RSV. A person’s risk for severe infection will also vary based on a number of factors, including age and health conditions .

The updated guidance acknowledges that we can simplify the recommendations for what to do after becoming infected with a respiratory virus, as part of the larger strategy to address spread.

The CDC also recently recommended that people over age 65 receive an additional dose of this year’s COVID vaccine . What drove that decision?

There are a few reasons behind this new recommendation for older adults . First, most severe COVID infections are occurring in individuals 65 years and older who have not been vaccinated recently. The CDC’s recommendation notes that more than half of COVID hospitalizations between October 2023 and December 2023 occurred in adults over 65.

Second, we know immunity after vaccination wanes over a few months, so an additional dose will provide renewed protection through the spring. New COVID variants like JN.1 that are circulating now have some mutations that improve their ability to evade vaccine-induced immunity, but the antibodies made through vaccination still recognize them. It’s not a perfect match, but a second dose of this year’s vaccine will provide protection against current variants to an age group at increased risk of severe illness, hospitalization, and death.

When should people over 65 get this additional dose of the current COVID vaccine?

The recommendation from the CDC is for people 65 and older who have already received one dose of the 2023-24 COVID vaccine to get a second shot at least four months after their most recent dose .

For people in that age group who haven’t had the 2023-24 vaccine, there’s no need to wait. They can get their shot now to be protected through the spring.

Will there be an updated COVID-19 vaccine for these newer variants?

We can likely expect to see a new COVID-19 vaccine available this fall, just like we see new, updated influenza vaccines each fall. This spring—typically around May—a decision will be made on which variants the updated vaccine will be designed around, and like we saw in 2023, the new vaccine will be available in the fall as we head into the typical respiratory virus season.

Aliza Rosen is a digital content strategist in the Office of External Affairs at the Johns Hopkins Bloomberg School of Public Health.

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Business Closures, Stay-at-Home Restrictions, and COVID-19 Testing Outcomes in New York City

ORIGINAL RESEARCH — Volume 17 — September 17, 2020

George J. Borjas, PhD 1 ( View author affiliations )

Suggested citation for this article: Borjas GJ. Business Closures, Stay-at-Home Restrictions, and COVID-19 Testing Outcomes in New York City. Prev Chronic Dis 2020;17:200264. DOI: http://dx.doi.org/10.5888/pcd17.200264 .

PEER REVIEWED

Introduction

Acknowledgments, author information.

What is already known on this topic?

Little is known about whether the business closures and restrictions on out-of-home activities mandated during the coronavirus disease 2019 (COVID-19) pandemic by many government units in the United States and abroad helped contain the spread of the virus.

What is added by this report?

This article examines daily testing data for New York City to determine if the economic and behavioral restrictions imposed by government policies limited the spread of COVID-19 in a dense urban setting.

What are the implications for public health practice?

These data suggest that the policy measures decreased the likelihood of positive results in COVID-19 tests. The study identifies specific policy tools that may be successfully used when comparable health crises arise in the future.

In response to the coronavirus disease 2019 (COVID-19) pandemic, New York City closed all nonessential businesses and restricted the out-of-home activities of residents as of March 22, 2020. This order affected different neighborhoods differently, as stores and workplaces are not randomly distributed across the city, and different populations may have responded differently to the out-of-home restrictions. This study examines how the business closures and activity restrictions affected COVID-19 testing results. An evaluation of whether such actions slowed the spread of the pandemic is a crucial step in designing effective public health policies.

Daily data on the fraction of COVID-19 tests yielding a positive result at the zip code level were analyzed in relation to the number of visits to local businesses (based on smartphone location) and the number of smartphones that stayed fixed at their home location. The regression model also included vectors of fixed effects for the day of the week, the calendar date, and the zip code of residence.

A large number of visits to local businesses increased the positivity rate of COVID-19 tests, while a large number of smartphones that stayed at home decreased it. A doubling in the relative number of visits increases the positivity rate by about 12.4 percentage points (95% CI, 5.3 to 19.6). A doubling in the relative number of stay-at-home devices lowered it by 2.0 percentage points (95% CI, −2.9 to −1.2). The business closures and out-of-home activity restrictions decreased the positivity rate, accounting for approximately 25% of the decline observed in April and May 2020.

Policy measures decreased the likelihood of positive results in COVID-19 tests. These specific policy tools may be successfully used when comparable health crises arise in the future.

The New York metropolitan area quickly became the epicenter of the coronavirus disease 2019 (COVID-19) pandemic in the United States. The first test in New York City for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, was administered on January 29, 2020, with the first positive result not confirmed until February 23, 2020 (1). By the end of March 2020, New York City had 67,789 people infected with the virus, and 2,193 people had died from the disease; by July 15, some 260,176 people had been infected and the total number of confirmed deaths was 18,756 (1).

These citywide statistics mask a lot of variation in testing outcomes across geographic areas in the city. Some New York City neighborhoods (as demarcated by zip code) were heavily affected while others were relatively unscathed. There is evidence that the initial testing resources were more readily available to people residing in wealthier neighborhoods and that people in those neighborhoods were less likely to test positive (2–5). In contrast, people residing in racial/ethnic minority neighborhoods, particularly neighborhoods with a large African American or Hispanic population, tended to test positive at much higher rates (2).

Effective on March 22, 2020, the state government issued a “New York State on PAUSE” executive order that closed all nonessential businesses, prohibited nonessential gatherings of individuals outside their homes, and limited outdoor recreational activities (6). The business closures affected different neighborhoods differently, as the location of stores and workplaces is not randomly distributed across New York City. Moreover, although government officials did not proclaim a stay-at-home order, the prohibition on nonessential gatherings effectively compelled people to spend a large fraction of their time at home. Different demographic or socioeconomic groups may differ in their propensities or opportunities to adhere to the curbs on out-of-home activities. These differences in the impact of the business closures or out-of-home activity restrictions may have created further geographic disparities in testing outcomes.

This study merges daily data on testing outcomes at the zip code level in New York City with information on the number of visitors to local points of interest (such as stores, restaurants, parks, hospitals, or museums) and the number of people who limited their out-of-home activities. Previous research on the spread of pandemic diseases, including COVID-19 and the 2009 H1N1 influenza, emphasizes the key role played by spatial diffusion (7–11). An understanding of the determinants of spatial transmission at the various stages of a pandemic is critical for the design of public health policies that seek to halt the spread of the pandemic or reduce the possibility of new outbreaks after the initial wave.

This study examines the geographic dispersion observed in the positivity rate across New York City neighborhoods to determine if the economic and behavioral restrictions imposed by the executive order limited the spread of COVID-19 in a dense urban setting. Such an evaluation can help identify which types of interventions are most effective in reducing the mortality and disease caused by pandemics. The evaluation can also inform the tradeoff between improved public health outcomes and the cost of limitations on social and economic activity.

I conducted a statistical analysis of COVID-19 testing data compiled by the New York City Department of Health and Mental Hygiene (DOH) that provides information on test results at the zip code level (12). The data are available beginning on April 1, 2020, and give a daily cumulative count of the number of tests and positive results for residents in each of 177 zip codes in the city. The sample period used in this study ended on May 31, 2020 (just before the disruption in the social distancing protocols during the protests of late May and early June 2020 might affect testing outcomes).

I merged the testing data with smartphone location information compiled by SafeGraph, a private company that partners with mobile applications to collect location data from 35 million mobile devices. The inclusion of a mobile device in the SafeGraph sample is not random. It only includes those users who gave applications opt-in consent to collect anonymous location data (13). The location data report the daily number of visitors to specific places in each zip code and the mobility patterns of residents in the neighborhood. The study uses a regression model that exploits the variation in testing outcomes over time and across zip codes to establish if the business closures and out-of-home activity restrictions affected how COVID-19 spread through the city.

Study measures

Positivity rate.

Citywide data on the daily number of tests and number of positive tests are available from the New York City DOH. Beginning on April 1, 2020, DOH also began to release daily information on the cumulative number of tests administered and the cumulative number of positive results for people residing in each of 177 zip codes. The daily report often allocates a small number of tests to a nonidentifiable geographic area (on average, the zip code is missing for 1.2% of daily tests and 2.1% of positive results). The statistical analysis excludes the test results that were not allocated to a particular zip code.

The information reported by DOH is not complete. The agency did not release the cumulative counts on 1 day in the sample period, and the released data were not usable on 2 other days (eg, the cumulative counts reported for a given day were identical to or smaller than the cumulative counts reported the day before). I corrected these inconsistencies, which affected 4.9% of the observations in the sample period, by linearly interpolating the cumulative counts from the day before and the day after the missing data. Test information at the zip code level is not available for the critical month of March (the month when the virus began to spread rapidly and the executive order was issued).

The cumulative number of tests and positive results was converted into a daily number by differencing the day-to-day cumulative totals. The positivity rate on any given day is defined as the percentage of tests administered that day that yielded a positive result for COVID-19. This statistic is available daily from April 2, 2020, through May 31, 2020, for each zip code in the city.

Business activity index

I used the Weekly Patterns places data from SafeGraph to construct an index of the number of people who visited any point of interest in a zip code on any given day (14). The data available from SafeGraph report the number of smartphone devices that visited every point of interest in the zip code on any given day.

A business activity index was constructed by first adding up all visits on a given day across all points of interest in a zip code. This sum was then converted into a rate per 1,000 people in the zip code, where the zip code’s population is an intercensal estimate produced by the DOH and the Department of City Planning (12). To ease the interpretation of the results, the business activity index is normalized to equal 100 for the entire city in the prepandemic period of February 4 through 6. This normalization allows the value of the index for any zip code at any point in time to be interpreted as percentage deviations from the prepandemic citywide average.

The testing for COVID-19 in the first months of the pandemic was targeted at people who had developed specific symptoms. The median number of days from exposure to the onset of symptoms is estimated to be 5.1 days, with 72.5% of cases observed between 2.2 and 6.7 days (15). The regression analysis would then relate testing outcomes on any given day t to the average value of the business activity index in the zip code 3 to 7 days prior (eg, the testing outcomes on May 11 are related to the average value of the index between May 4 and May 8).

Stay-at-home index

I used SafeGraph’s Social Distancing Metrics data to construct an index that approximates the fraction of people in a neighborhood that stayed at home during the pandemic (16). SafeGraph assigns each smartphone device a “home location,” the most common nighttime location of that device over a prior 6-week period (with a precision of 100 m 2 ). For each census block group, the SafeGraph data then reports the number of devices that did not leave their home location on any given day.

The data were aggregated to the zip code level using a crosswalk file produced by the US Department of Housing and Urban Development that allocates census tracts to zip codes (17). For each day–zip code combination, I defined the stay-at-home index as the number of devices that did not leave their home location per 1,000 people in the zip code. The stay-at-home index was also normalized to equal 100 for the entire city during February 4 through 6. The regression analysis would then relate the testing outcomes on any given day to the average value of the stay-at-home index 3 to 7 days prior.

Statistical analysis

The analysis used a linear regression model to determine the association between the positivity rate for residents in a zip code on any given day and the lagged values of the business activity and stay-at-home indices. The data consisted of 1 observation per zip code per day from April 2 through May 31. The regression has 10,554 observations (177 zip codes each observed 60 days, minus the day–zip code combinations where no tests were administered).

The regression model also included vectors of fixed effects to net out other factors that affect the positivity rate. These additional regressors included a vector of day-of-week fixed effects (eg, Monday, Tuesday). The frequency of testing is typically lower on weekends, and the reported outcome of those tests may be delayed until the beginning of the work week. The regression included a vector of fixed effects giving the actual calendar date in which the test was given (eg, April 13 or May 5). These calendar date fixed effects help net out the citywide trend in the positivity rate. Finally, the regression included a vector of zip code fixed effects. These fixed effects net out factors that permanently affected the positivity rate in a particular neighborhood throughout the April–May period.

The inclusion of the zip code fixed effects controls for geographic differences in socioeconomic characteristics that are specific to the zip code and that did not change over the sample period. Put differently, although the regression does not include any neighborhood-specific socioeconomic status variables (such as ethnicity, race, sex, or income that could be calculated from the annual American Community Survey data), the impact of these characteristics is effectively subsumed by the zip code fixed effects.

The citywide trend in the test positivity rate in New York City reached a maximum of 71% on March 28 and declined steadily in the next 2 months ( Figure ). By May 31, only 4% of tests had a positive outcome. The business activity and stay-at-home indices averaged across zip codes were near their prepandemic value of 100 until about the middle of March, just before the executive order went into effect ( Figure ). At that time, the business activity index rapidly declined and bottomed out on April 16 when it reached a low of 22.9. In contrast, the stay-at-home index rapidly increased, reaching a peak of 206.6 on April 8.

These citywide trends mask the large variance in testing outcomes across neighborhoods. Table 1 shows some of the variation by reporting the positivity rate at 3 different points in time for the most populous zip code in each of the 5 boroughs. To ensure comparability, all time periods refer to a Tuesday–Thursday time frame. The Manhattan neighborhood (Manhattan Valley/Morningside Heights/Upper West Side) had a relatively low positivity rate of 58.9% in early April. This contrasts with the 85.1% positivity rate in the Corona/North Corona neighborhoods of Queens.

Table 1 also shows the geographic variation in the speed at which the positivity rate fell. The positivity rate in the most populous Bronx neighborhood (Allerton/Norwood/ Pelham Parkway/Williamsbridge) declined from 65.9 to 17.4 between early April and early May. In contrast, the rate in the Williamsburg neighborhood of Brooklyn started off at roughly the same level in April (63.9) but had dropped to 9.1 by early May.

Table 2 documents the large geographic differences in the business activity and stay-at-home indices, both at a point in time and in their rate of change as the pandemic took hold. The trend in the business activity index shows that the number of visitors to a point of interest in Manhattan fell by about 46 points (from 66 to 20) between February (before any mobility restrictions) and early May. The decline was less steep in Queens, where the index fell by 34 points (from 62 to 28). Similarly, the stay-at-home index rose faster in the Queens neighborhood than in the Bronx one. In Queens, the stay-at-home index increased from 89 to 163 between February and early May, while it rose from 100 to 144 in the Bronx.

The correlation coefficient between the business activity and stay-at-home indices is 0.39. This correlation is modest and suggests that multicollinearity between the 2 indices does not play a role in the estimation of the regression model. In contrast, the correlation between the lagged and current values of the indices is high: 0.94 for the business activity index and 0.92 for the stay-at-home index.

Both the business activity and stay-at-home indices have statistically significant effects on the positivity rate ( Table 3 ). A 100-unit increase in the business activity index (implying a doubling in the relative number of visitors from the citywide prepandemic average) increased the positivity rate by 12.4 percentage points ( P = .001; 95% CI, 5.3–19.6). A 100-unit increase in the stay-at-home index (implying a doubling in the relative number of devices that did not leave the home location) decreased the positivity rate by 2.0 percentage points ( P < .001; 95% CI, −2.9 to −1.2). The regression model explains 82% of the variation in the positivity rate across neighborhoods and over time ( Table 3 ).

The positivity rate in the city decreased from about 54% in early April to 14% in early May ( Table 1 ). The business activity index decreased by about 70 points from the prepandemic baseline to early May, producing a 9-percentage point drop in the positivity rate. The stay-at-home index increased by about 80 points, producing a 2-percentage point drop in the positivity rate. The total direct impact of the 2 indices, therefore, accounted for approximately 25% of the observed 40-percentage point decline in the positivity rate.

The regression analysis indicates that the nonessential business closures and out-of-home activity restrictions adopted in New York City decreased the positivity rate of COVID-19 tests. The quantitative size of the impact, however, was relatively small.

The regression model explains 82% of the variation in the positivity rate across neighborhoods and over time, but the 2 indices accounted for only 25% of the drop in the average positivity rate for the city. The positivity rate varied considerably across New York City neighborhoods and declined noticeably during the sample period. The zip code fixed effects explain a large part of this cross-section variation, and several studies suggest that it is partly attributable to neighborhood differences in such variables as household income and racial composition (2–5). At the same time, the calendar date fixed effects net out the steep citywide decline. The 2 sets of fixed effects help produce the large explanatory power of the regression.

The inclusion of the fixed effects in the regression model implies that the impact of the business activity and stay-at-home indices is identified by correlating the indices with the positivity rate (net of the citywide trend) within a specific zip code. Put differently, the regression coefficients only capture the impact of a change in the local index on the net positivity rate of the typical zip code. The indices could account for more of the citywide trend if there were substantial “spillovers” across neighborhoods. A change in the indices in one zip code would then affect the positivity rate in other zip codes, and the spatial autocorrelation might generate part of the citywide decline. A more detailed examination of the SafeGraph data, taking into account the geographic origin of visitors or the stay-at-home decisions of residents in nearby neighborhoods, could potentially be used to directly estimate the spatial autocorrelation (18).

Although the “distancing” produced by the mandated business closures and by the restrictions on nonessential out-of-home activities slowed the spread of the COVID-19 virus, the analysis also suggests that business closures played a disproportionately larger role in reducing the positivity rate. This finding can inform the debate over the tradeoffs faced in the development of anticontagion policies and may affect calculations of the net economic cost of those policies (19). The economic disruptions resulting from the mandated business closures, which included a historic increase in the number of people out of work, may be very damaging (20,21). At the same time, those closures led to a considerable decrease in the positivity rate, resulting in fewer serious illnesses (and fatalities) and potentially large reductions in health care costs.

This study has several limitations. The data on testing outcomes at the zip code level did not become available until April 1, 2020 (after the executive order went into effect on March 22). Ideally, the analysis would have used data on positivity rates in the various zip codes both before and after the regulations began to affect behavior. The large change observed in the business activity and stay-at-home indices from the prepandemic baseline might lead to more precise estimates of their impact on the positivity rate.

There are also limitations with the testing data released by the New York City DOH: the testing results may not have been released on a particular day; the counts were sometimes inconsistent across adjacent days; and the date the testing data were reported may differ from the date the test was actually administered. This measurement error likely biases the regression coefficients. If the errors were random, the measured impacts of the business closures and restrictions on out-of-home activities are probably underestimated.

The business activity and stay-at-home indices used in the analysis may have limited informational content. The number of visitors to various points of interest does not directly measure how people who reside in the neighborhood are exposed to and interact with the visitors. The exposure might vary depending on the nature of the point of interest (eg, a park is different than the small corner grocery store). Similarly, the number of smartphone devices that have not left their home location on any given day is an incomplete measure of what social distancing and “shelter-at-home” entails. Moreover, the nature of the New York State on PAUSE executive order produced an interaction between the 2 indices: business closures likely increased the value of the stay-at-home index. This interdependence makes it difficult to forecast the impact of a narrower policy.

The cell phone location data that can potentially increase our understanding of mobility patterns in the population are also imperfectly measured. The people who own the sampled devices (and are captured by the SafeGraph algorithm) may not form a representative sample of the population; the available location data do not adjust for ownership of multiple (or zero) devices; and the definition of the “home location” for any particular device is sensitive to idiosyncratic variation across individuals, such as working a night shift.

Finally, part of the positivity rate variation across zip codes likely arises because COVID-19 testing resources were not allocated randomly across neighborhoods (at least in the initial stage of the pandemic). Although the regression analysis partially addresses this problem by including a vector of zip code fixed effects, these fixed effects only net out the impact of geographic factors that had a constant impact on the positivity rate of the neighborhood at all times. It is possible, however, that the nonrandomness in the allocation of testing resources was addressed as the volume of testing increased in April and May, so that the actual impact of the geographic characteristics presumably captured by the zip code fixed effects would have changed over time.

Further analysis of testing data that might eventually become available at the individual level would help resolve some of these problems. The individual-level data would allow a research design that links test outcomes to both individual and area characteristics.

The author has no relationships that could be construed as a conflict of interest. No financial support or funding was received for this study. No copyrighted materials were used in this study or in the writing of this article.

Corresponding Author: George J. Borjas, PhD, Harvard Kennedy School, 79 JFK Street, Cambridge, MA 02138. Telephone: 617-495-1393. Email: [email protected] .

Author Affiliations: 1 Harvard Kennedy School, Cambridge, Massachusetts.

• New York City Department of Health and Mental Hygiene. Data files: testing.csv, tests.csv, and probable-confirmed-dod.csv. https://github.com/nychealth/coronavirus-data. Accessed April 1, 2020 and August 13, 2020.
• Borjas GJ. Demographic determinants of testing incidence and COVID-19 infections in New York City neighborhoods. Cambridge (MA): National Bureau of Economic Research; 2020. NBER Working Paper No. 26952.
• Almagro M, Orane-Hutchingson A. The determinants of the differential exposure to COVID-19 in New York City and their evolution over time. Covid Economics: Vetted and Real Time Papers 2020;(13):31–50.
• Schmitt-Grohé S, Teoh K, Uribe M. Covid-19: testing inequality in New York City. Covid Economics: Vetted and Real Time Papers 2020;(8):27–43. CrossRef
• Chen Y, Jiao JJ, Bai S, Lindquist J. Modeling the spatial factors of COVID-19 in New York City. SSRN working paper, May 26, 2020.
• New York State Department of Health. New York State on PAUSE, 10 point plan. March 20, 2020. https://coronavirus.health.ny.gov/new-york-state-pause. Accessed June 3, 2020.
• Desjardins MR, Hohl A, Delmelle EM. Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: detecting and evaluating emerging clusters. Appl Geogr 2020;118:102202. CrossRef PubMed
• Hohl A, Delmelle EM, Desjardins MR. Rapid detection of COVID-19 clusters in the United States using a prospective space-time scan statistic: an update. SIGSPATIAL Special 2020;12(1):27–33. CrossRef
• Kang D, Choi H, Kim JH, Choi J. Spatial epidemic dynamics of the COVID-19 outbreak in China. Int J Infect Dis 2020;94:96–102. CrossRef PubMed
• Gog JR, Ballesteros S, Viboud C, Simonsen L, Bjornstad ON, Shaman J, et al. Spatial transmission of 2009 pandemic influenza in the US. PLoS Comput Biol 2014;10(6):e1003635. CrossRef PubMed
• Apolloni A, Poletto C, Colizza V. Age-specific contacts and travel patterns in the spatial spread of 2009 H1N1 influenza pandemic. BMC Infect Dis 2013;13(1):176. CrossRef PubMed
• New York City Department of Health and Mental Hygiene. Data files: tests-by-czta.csv prior to May 18, and data-by-modzcta.csv beginning on May 18. https://github.com/nychealth/coronavirus-data. Accessed daily, April 1 through June 3, 2020.
• SafeGraph. FAQs. https://docs.safegraph.com/docs/faqs. Accessed July 6, 2020.
• SafeGraph. Weekly Patterns v2. https://docs.safegraph.com/docs/weekly-patterns. Accessed June 3, 2020.
• Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann Intern Med 2020;172(9):577–82. CrossRef PubMed
• SafeGraph. Social Distancing Metrics, v2.1. https://docs.safegraph.com/docs/social-distancing-metrics. Accessed June 3, 2020.
• US Department of Housing and Urban Development, Office of Policy Development and Research. Data file: tract-zip-122019.xlsx. https://www.huduser.gov/portal/datasets/usps_crosswalk.html. Accessed June 10, 2020.
• Anselin L, Arribas-Bel D. Spatial fixed effects and spatial dependence in a single cross-section. Pap Reg Sci 2013;92(1):3–17. CrossRef
• Hsiang S, Allen D, Annan-Phan S, Bell K, Bolliger I, Chong T, et al. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature 2020. CrossRef PubMed
• Coibion O, Gorodnichenko Y, Weber M. The cost of the Covid-19 crisis: lockdowns, macroeconomic expectations, and consumer spending. Cambridge (MA): National Bureau of Economic Research; 2020. NBER Working Paper No. 27141.
• Cajner T, Crane LD, Decker RA, Grigsby J, Hamins-Puertolas A, Hurst E, et al. The U.S. labor market during the beginning of the pandemic recession. Cambridge (MA): National Bureau of Economic Research; 2020. NBER Working Paper No. 27159.

a Manhattan, zip code 10025 (Manhattan Valley/Morningside Heights/Upper West Side); The Bronx, 10467 (Allerton/Norwood/ Pelham Parkway/Williamsbridge); Queens, 11368 (Corona/North Corona); Brooklyn, 11211 (East Williamsburg/Williamsburg [North]/Williamsburg [South]); and Staten Island, 10314 (Bloomfield/Freshkills Park). b The positivity rate gives the percentage of tests administered in a particular geographic area on a given day that yielded a positive result.

a Manhattan, zip code 10025 (Manhattan Valley/Morningside Heights/Upper West Side); The Bronx, 10467 (Allerton/Norwood/ Pelham Parkway/Williamsbridge); Queens, 11368 (Corona/North Corona); Brooklyn, 11211 (East Williamsburg/Williamsburg [North]/Williamsburg [South]); Staten Island, 10314 (Bloomfield/Freshkills Park). b Average number of visits to a point of interest (such places as stores, restaurants, parks, hospitals, or museums) per 1,000 people in the zip code, normalized to equal 100 for the entire city in the prepandemic period of February 4–6. c Average number of smartphone devices that did not leave the home location per 1,000 people in the zip code, normalized to equal 100 for the entire city in the prepandemic period of February 4–6.

Abbreviation: NA, not applicable; SD, standard deviation. a The business activity index gives the average number of visits to a point of interest (such places as stores, restaurants, parks, hospitals, or museums) per 1,000 people in the zip code. The stay-at-home index gives the average number of smartphone devices that did not leave the home location per 1,000 people. Both indices are normalized to equal 100 for the entire city in the prepandemic period of February 4–6. The regression uses the average lagged value of the indices 3 to 7 days before the administration of the test. b Regression coefficient from linear regression that also includes day-of-week, calendar date, and zip code fixed effects. The dependent variable gives the daily percentage of tests administered to residents of a zip code that gave a positive result (mean [SD], 26.4 [23.1]). The standard error of β is clustered at the zip code level. The regression excludes zip code–day combinations where no tests were administered.

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.

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Research Article

Tele-medicine controlled hospital at home is associated with better outcomes than hospital stay

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing

Affiliation Faculty of Data and Decision Sciences, Technion–Israel Institute of Technology, Haifa, Israel

Roles Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

Affiliation Biostatistics and Biomathematics Unit, Gertner Institute of Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel Hashomer, Israel

Roles Conceptualization, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

Roles Data curation, Investigation, Methodology, Writing – original draft

Affiliation Sheba Beyond Virtual Hospital, Chaim Sheba Medical Center, Ramat Gan, Israel

Roles Conceptualization, Data curation, Investigation, Methodology, Project administration, Software, Supervision, Writing – original draft, Writing – review & editing

Roles Conceptualization, Methodology, Project administration, Supervision, Validation, Writing – review & editing

Affiliation Management Wing, Chaim Sheba Medical Center, Ramat Gan, Israel

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Sheba Beyond Virtual Hospital, Chaim Sheba Medical Center, Ramat Gan, Israel, Education Authority, Chaim Sheba Medical Center, Ramat Gan, Israel, Faculty of Healthcare and Medicine, Tel Aviv University, Tel-Aviv, Israel

• Noa Zychlinski, 
• Ronen Fluss, 
• Yair Goldberg, 
• Daniel Zubli, 
• Galia Barkai, 
• Eyal Zimlichman, 

• Published: August 19, 2024
• https://doi.org/10.1371/journal.pone.0309077
• Peer Review
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Hospital-at-home (HAH) is increasingly becoming an alternative for in-hospital stay in selected clinical scenarios. Nevertheless, there is still a question whether HAH could be a viable option for acutely ill patients, otherwise hospitalized in departments of general-internal medicine.

This was a retrospective matched study, conducted at a telemedicine controlled HAH department, being part of a tertiary medical center. The objective was to compare clinical outcomes of acutely ill patients (both COVID-19 and non-COVID) admitted to either in-hospital or HAH. Non-COVID patients had one of three acute infectious diseases: urinary tract infections (UTI, either lower or upper), pneumonia, or cellulitis.

The analysis involved 159 HAH patients (64 COVID-19 and 95 non-COVID) who were compared to a matched sample of in-hospital patients (192 COVID-19 and 285 non-COVID). The median length-of-hospital stay (LOS) was 2 days shorter in the HAH for both COVID-19 patients (95% CI: 1–3; p = 0.008) and non-COVID patients (95% CI; 1–3; p < 0.001). The readmission rates within 30 days were not significantly different for both COVID-19 patients (Odds Ratio (OR) = 1; 95% CI: 0.49–2.04; p = 1) and non-COVID patients (OR = 0.7; 95% CI; 0.39–1.28; p = 0.25). The differences remained insignificant within one year. The risk of death within 30 days was significantly lower in the HAH group for COVID-19 patients (OR = 0.34; 95% CI: 0.11–0.86; p = 0.018) and non-COVID patients (OR = 0.38; 95% CI: 0.14–0.9; p = 0.019). For one year survival period, the differences were significant for COVID-19 patients (OR = 0.5; 95% CI: 0.31–0.9; p = 0.044) and insignificant for non-COVID patients (OR = 0.63; 95% CI: 0.4–1; p = 0.052).

Conclusions

Care for acutely ill patients in the setting of telemedicine-based hospital at home has the potential to reduce hospitalization length without increasing readmission risk and to reduce both 30 days and one-year mortality rates.

Citation: Zychlinski N, Fluss R, Goldberg Y, Zubli D, Barkai G, Zimlichman E, et al. (2024) Tele-medicine controlled hospital at home is associated with better outcomes than hospital stay. PLoS ONE 19(8): e0309077. https://doi.org/10.1371/journal.pone.0309077

Editor: Filomena Pietrantonio, San Giuseppe Hospital, ITALY

Received: April 18, 2024; Accepted: August 5, 2024; Published: August 19, 2024

Copyright: © 2024 Zychlinski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All de-identified data will become available upon request from the Principal Investigator (Prof. Gad Segal, MD). This is a mandatory, legal term on behalf of our institutional ethics committee. Upon request, data will go through a double check of anonymization and then sent direct to whoever requested the data. Aside from the Principal Investigator, a non-author contact that can be addressed is our local IRB at the following email address: [email protected] . The aforementioned restrictions are warranted since the patients’ data could potentially include sensitive patients’ information.

Funding: The Research was supported in part by an Israel Science Foundation [Grant 277/21] and the Israel National Institute for Health Policy Research [Grant 2021/160/R]. Guarantor: No guarantees were given regarding this study.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Hospital-at-home (hah) services worldwide.

Globally, healthcare systems in general and their hospitalization arms in particular, are experiencing hardships in terms of infrastructure, resources, and lower availability of skilled healthcare professionals. These hardships were worsened, as stated by the World Economic Forum, by the unprecedented disruptions caused by the COVID-19 pandemic [ 1 ]. As a result, it was recently published, in a 2023 survey, that 46% of adults worldwide encounter limited access to treatment and prolonged waiting times to reach affordable health resources with lack of staff being the biggest challenge [ 2 ]. These challenges are being answered by social, financial, and healthcare organizations, with innovative approaches and solutions being advocated. One such approach was recently introduced by a global consulting firm, presenting the concept of hospitals without walls [ 3 ]. This wide-span concept of health without boundaries, includes the adoption of advanced high-technology in the service of telemedicine, serving as an enabler for making the HAH services the safest and most effective as can be attained. Recent years brought success in this realm, mainly with regard to COVID-19 patients [ 4 – 6 ].

Telemedicine-controlled hospital-at-home services

A predominantly important factor contributing to the prognosis of patients during hospitalization in an internal medicine department, is the experience of their attending, senior physicians. These practitioners are becoming less available and practically inexistent in some peripheral areas. One way of coping with this problem would depend on the ability of experienced, senior internal-medicine specialists to diagnose and treat their patients from a distance, upscaling their influence on population health. Recent advancements in telemedicine, another consequence of the COVID-19 pandemic, have paved the way for sophisticated remote medical services, introducing home hospitalization as a viable alternative to traditional on-site care. Recently published studies’ results, relating to the advantages of tele-monitoring and miniaturized technologies, either in the HAH settings or post-hospital-discharges plans, enhance our ability of rely on tele-medicine services in terms of patients’ safety, risks of re-admission and other, in-hospital related side events [ 7 – 9 ].

During the year 2020, Sheba Beyond was established as an integral part of the Sheba Medical Center, encompassing all tele-health services in this tertiary hospital. By enabling remote physical examinations, monitoring, and online rehabilitation programs, Sheba Beyond aims to make high-quality medical expertise accessible to broader audiences. This aligns with the growing expectation that remote hospitalization will become a widely available service among major hospital networks, across many specialties. During the past several years, the unique HAH service at Sheba Beyond served not only as a clinical service but also as a validation laboratory for essential, telemedicine technologies and methodologies: TytoCare © technology, serving as a remote, digital stethoscope, was clinically investigated, with measurements of physicians’ compliance [ 10 ], validity and inter-observers’ consensus of clinical interpretations [ 11 ]. Biobeat © technology for wireless, remote monitoring of several physiologic vital signs and parameters was validated for its reliability of telemetric transmission and comparison to overhead monitors, and potential to accumulate patients’ data that could foresee future patients’ deterioration [ 12 ]. A six-lead, self-handled electrocardiography (ECG) device transmitting heart rhythm description and analysis was also validated and the level of consensus of agreement was tested versus a gold-standard, legacy 12-lead ECG machine [ 13 ]. Alongside these technologies, methodologies of telemedicine based HAH were also investigated, such as a clinical pivotal trial done with a specialist in internal medicine, based within an in-hospital, internal medicine department, managed patients that stayed in their elderly home [ 14 ]. The ability of safeguarding acutely ill patients in the HAH setting was also shown to be feasible in a significant portion of patients, diagnosed as suffering from an acute, infectious disease, who demonstrate laboratory evidence of myocardial damage and still, are enjoying the efficacy and safety of the HAH service [ 15 ]. The concept of assimilating a virtual medicine-based department into the structure of a conventional medical center was also recently described [ 16 ].

Aim of the current study

Prior research has focused on the efficacy of telemedicine-based medical services to various patients’ populations including remote rehabilitation across various indications including deterioration of patients suffering from chronic congestive heart failure [ 17 ], sarcopenia [ 18 ], post-stroke recovery [ 19 ], exacerbation of chronic obstructive pulmonary disease (COPD) [ 20 ], and post-acute therapy [ 21 ]. However, limited attention has been directed towards investigating remote hospitalization of patients in the setting of acute illness, regularly directed to in-hospital stay in internal medicine wards. Some of the studies who addressed acute illness HAH services, included telemedicine visits in various proportions, however none of the programs were based on physician telemedicine visits [ 22 – 24 ].

This study focused on the distinctive remote telemedicine-based internal medicine model. Unlike traditional on-site admissions, patients underwent admission by a remote physician, receiving a personalized treatment plan that integrates home visits and adequate medical monitoring utilizing cutting-edge technologies. The full spectrum of nursing services was performed at patients’ homes, along with laboratory testing and chest x-rays as indicated by the attending physicians who delivered service via remote, telemedicine platforms. The present study investigated the efficacy and safety of this service in a retrospective comparison and analysis of both COVID-19 and non-COVID matched patient populations.

Study design and patients’ care

This study was performed by the Sheba Medical Center, 1,900 beds, tertiary hospital, largest of its kind in Israel. This was a retrospective matched study with 159 Sheba-Beyond hospitalizations (64 for COVID-19 and 95 for non-COVID) categorized as Group HAH. They were compared to a matched sample of controls, denoted as Group C, out of 6,817 patients who were hospitalized in the internal-medicine departments of Sheba Medical Center (2,242 for COVID-19 and 4,924 for one of three acute, infectious diseases: urinary tract infections (UTI, either lower or upper), pneumonia, or cellulitis) over the years 2021–2023 inclusive. The study included patients aged 18 and older. Respiratory and hemodynamically unstable patients as well as mild COVID-19 patients were excluded from the study. All patients’ data were extracted from their electronic medical records (EMR) which serve for clinical purposes. Fig 1 details the above patient consort flow and exclusion diagram.

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https://doi.org/10.1371/journal.pone.0309077.g001

Ethic statement: Data was mined after approval by a local, institutional review board (approval # SMC-21-8828) and after patients’ written consent was waived due to the retrospective nature of this study. Clinical data was approached during the months between August 2023 and February 2024.

Eligible patients for home hospitalization were transitioned to receive care in the comfort of their homes. The HAH team attending these patients consists of internal medicine specialists, licensed case management nurses, home visiting nurses, X-ray technicians and call center nurses. The patient receives daily a minimum of one remote physician visit, 2 nurses visit (1 at home) and an individualized treatment plan that may include imaging, blood testing and IV, and oral treatment. Medical directives, encompassing vital signs monitoring regimen and treatments, are either carried out by the patients themselves or administered by the nursing staff during scheduled home visits. A video conversation with the attending physician was conducted at least once daily, typically in the morning, with the visiting nurse present at the patient’s home. Video calls were done using a designated platform for telemedicine purposes (DATOS). During these sessions, a remote physical examination was facilitated using the TytoCare ® system. This digital platform incorporates a digital stethoscope enabling heart, lung and abdominal (peristalsis) auscultation, a digital otoscope for visualizing the tympanic membrane, a digital thermometer, and a tongue depressor for visual examination of the pharynx. The device guides patients (or their assistants) through the examination process and records data and visuals, which are then transmitted through the internet for review by the physician. Video conferences were conducted whenever a specialist consultation was deemed necessary, with one of Sheba’s specialists, as regularly done in the in-hospital settings. Each daily visit was documented in the patient’s electronic medical record, including orders for blood tests, oxygen enrichment, prescribed medications, and recommendations for either hospital readmission in case of deterioration or discharge in case of improvement.

In the event of patient deterioration during home hospitalization, immediate coordination with the physician would facilitate the patient’s return to the hospital’s emergency department. Conversely, when the patient was ready for discharge, a discharge letter was sent, and the ongoing treatment plan was communicated to the staff via phone to ensure optimal continuity of care. The attending physician remained available for further consultation regarding the patient’s care for an additional week after discharge.

Data mining and analysis

All relevant patients’ characteristics were extracted from their EMR: age, severity of disease as categorized by clinicians for COVID-19 patients, gender; chronic / background diagnoses and chronic medications. We gathered individual diagnoses to the following silos: active malignancy, past malignancy, hematologic diseases, neurologic, metabolic, cardiovascular, respiratory, autoimmune, and gastrointestinal diseases. Similarly, chronic medications were also listed and grouped, as relating to either malignant, neurologic, metabolic, cardiovascular, respiratory, autoimmune, or gastrointestinal, as well as chronic medication for ophthalmic use.

The analysis compared outcomes of COVID-19 and non-COVID patients in groups HAH and C. Clinical outcome measures included mean length of stay (LOS) in days, readmission rates within 30 days or one year from discharge and mortality rates from admission within the same time frames. In the readmission analyses, patients who died before discharge were excluded and patients who died within the follow-up period were regarded as readmissions.

We used propensity scores (PSs) to match patients from the HAH with those from C group. Four risk factors (RF) deemed relevant, up front to the study outcomes, and were therefore included in the PS for patients’ matching: age, presence of active malignancy, dementia, and chronic kidney disease (CKD). For COVID-19 patients, grade of disease severity (as indicated during the period of hospitalization) was also incorporated. Additional risk factors were scrutinized individually in separate univariate logistic regressions for each one of the clinical outcomes serving as the dependent variable, while controlling for the four risk factors mentioned above. We retained those risk factors which had a p value less than 0.05 in at least one outcome. PSs were obtained, representing the estimated probability of being in the HAH group, using a logistic regression that included the RFs selected in the previous stage as predictors. Controls were matched to patients in the HAH group using the PS with a ratio of 1:3. In case of COVID-19 hospitalizations, we enforced exact matching of severity. We assessed the similarity of the resulting matched groups both graphically and by the calculating the standardized mean differences (SMD) of the RFs and PS. An absolute SMD less than 0.25 is usually regarded as a good balance [ 25 ].

A univariate analysis was used to compare the matched groups. The LOS was tested using the Wilcoxon test and the 95% CI was obtained by bootstrap resampling. The mortality rates were compared using the Fisher exact test. The readmission rates were compared using a weighted logistic regression. The weights were used to keep the balance of the matched samples after we excluded those who died before discharge. We also used Cox regression to compare time to death or readmission within one year.

Table 1 includes demographic and clinical features of all patients included in the two HAH groups and the two control groups. All the reported ratios compared the HAH group to the C group.

https://doi.org/10.1371/journal.pone.0309077.t001

Length of Stay (LOS)

The median LOS among COVID-19 patients was 5 and 7 in patients’ groups HAH and C, respectively, with a statistically significant 2-day difference, and the 95% CI was 1–3 (p value = 0.008). Among non-COVID patients, the median LOS was 2 and 4 days, in groups HAH and C, respectively, with a statistically significant 2-day difference, and the 95% CI was 1–3 (p value < 0.001).

Readmission within 30 days and one year duration

The Odds Ratio (OR) for readmission within 30 days among COVID-19 patients was 1, and the 95% CI was 0.49–2.04 (p value = 1). For non-COVID patients, the OR was 0.7 and the 95% CI was 0.39–1.28 (p value = 0.25). The OR for readmission within one year among COVID-19 patients was 1.05 and the 95% CI was 0.57–1.93 (p value = 0.8). For non-COVID patients, the OR was 0.67 and the 95% CI was 0.41–1.09 (p value = 0.11).

The Hazard Ratio (HR) for the time to readmission within one-year from discharge for COVID-19 patients was 1.06 and the 95% CI was 0.67–1.96 (p value = 0.8). For non-COVID patients, the HR was 0.81 and the 95% CI was 0.59–1.13 (p value = 0.21). Fig 2 shows the one-year Kaplan–Meier estimated survival curves for re-readmission among the COVID-19 and non-COVID patients: HAH versus their controls. For COVID-19 patients, the control and HAH groups are very close with no significant difference. For non-COVID, the probability of no readmission was lower, though not significantly, in the HAH group.

https://doi.org/10.1371/journal.pone.0309077.g002

Mortality within 30 days and one year duration

The OR for mortality within 30 days among COVID-19 patients was 0.34 and the 95% CI was 0.11–0.86 (p value = 0.018). Among non-COVID patients, the OR was 0.38 and the 95% CI was 0.14–0.90 (p value = 0.019). The OR for mortality within one year among COVID-19 patients was 0.51 and the 95% CI was 0.24–1.02 (p value = 0.046 using Fisher exact test). Among non-COVID patients, the OR was 0.64 and the exact 95% CI was 0.37–1.10 (p value = 0.103 using Fisher exact test).

The HR for COVID-19 patients was 0.55, the 95% CI was 0.31–0.98 (p value = 0.044). For non-COVID patients, the HR was 0.63 and the 95% CI was 0.40–1.00 (p value = 0.052). Fig 3 shows the one-year Kaplan–Meier estimated survival curves for COVID-19 and non-COVID patients: HAH versus their controls.

https://doi.org/10.1371/journal.pone.0309077.g003

Hospital-at-home as an alternative to in-hospital stay

The results of this study were reached after strict matching of the compared patients’ groups. The scrutinizing match process has significantly diminished our study population reaching the final analysis and still, statistically significant results were reached, enabling further conclusions to be drawn relating to the study clinical outcomes. In face of our results, the initial motivation to show that HAH would be non-inferior to in-hospital stay, should be substituted with the notion that HAH, when delivered in the above-described meticulous methodology and appropriate technologies, could be superior to in-hospital stay in terms of less readmissions and longer patients’ survival. It should be stated that during this study, we used technologies and staff that are being used in the routine HAH service in our medical center. All study results and the derived insights should be counted regarding the above. We foresee even better possible achievements as technology will continue to evolve and designated healthcare professionals gain even more experience in the realm of HAH.

The novelty of the described HAH setting

The main novelty in the above results is the fact that the HAH arm was based on telemedicine performed by the attending physician. Reliance on remote care technology enabled us to employ an experienced specialist in internal medicine, otherwise unavailable in case there was a need for large-scale home visits. We believe that the experience of the attending physicians was key to achieving superior clinical outcomes in the HAH arm over the C arm of this study. Also, the technologies employed were already used for several years (e.g. DATOS, a designated telemedicine-based platform) and already validated in clinical use cases. These technologies enable bridging the geographical gap between the physician and the patient. Moreover, the nursing staff, delivering both diagnostic and therapeutic measures to our patients is based on experienced nurses, almost all with several years of experience, holding advanced nursing degrees and qualifications.

Clinical outcomes

Relating to the shortened length of stay in the HAH group: this could potentially help HAH organizations in their struggle to prove affordability. Nevertheless, we do not see the shorter LOS as a predominant achievement in the HAH bundle. As in-hospital departments become more crowded, length of in-hospital stay will inevitably become shorter–not due to better treatment but due to earlier, at times, too early patients’ discharge. Therefore, we anticipate that HAH will prove to be as long as in-hospital stays and even longer. At home, the patients are not expected to make place to the next admissions and the length of hospitalization can and should stem only from the measures of good clinical practice.

Relating to the lower rates of re-admissions. These would have been easier to explain if indeed the HAH LOS were longer, providing the optimal treatment length needed. Since this was not the case, we assume that indeed, the experience of the attending physicians made the HAH hospitalizations more effective, putting the patients “on track” to better health, avoiding a larger number of re-admissions. It should be stated however that the retrospective nature of this study could be associated with a bias: it is possible that those patients that were suited to HAH continued to prefer their home environment and succeeded in maintaining themselves in the community while those who stayed in-hospital were more easily re-admitted. The fact that amongst COVID-19 patients there was no difference in readmissions could be related to the fact that for many patients, in-hospital stay due to COVID-19 was compulsory by healthcare regulations and the same for re-admission. Therefore, the motivation of patients contributed less to this end point. This study was not designed to assess financial endpoints, typically affected by re-admissions. Such analyses should be sought in future, prospective studies.

Regarding the end point of survival at 30 days, HAH was associated with less mortality, in a statistically significant manner, for both COVID-19 and non-COVID patients. We assume that this difference stem from the higher chances of in-hospital acquisition of secondary infections, practically nonexistent in the HAH setting. However, it should be stated that hospital-aquiered infections and other, hospital–acquired complications were not monitored in this study nor we recorded the causes of death. Relating to the one-year survival rates: these continued to be significantly higher for HAH patients in the COVID-19 patients’ group while losing statistical significance in the non-COVID-19 patients. This could be explained by the significant frailty characteristics of post-COVID-19 patients and the fact that on the one-year scale, in-hospital complications became less relevant for the non-COVID patients.

The place of our results in perspective of current literature

Freund et al. (2023) compared early discharge of COVID-19 patients with controls and found out that a transference of such patients to their homes with continuing oxygen support was associated with shortened in-hospital stay but also with increased rate of readmissions and no benefits relating to long-term outcomes [ 26 ]. Their findings emphasize the difference between continuing medical attention and support in the community and the full bundle of services in the form of HAH. In their comprehensive meta-analysis, Chauhan, and McAlister (2022) reviewed 24 randomized clinical trials, including 10,876 patients, comparing post-discharge transference of patients to continuing attendance of virtual wards (VW) versus usual post discharge community care [ 27 ]. Although these were heterogenous services, none at the full scale HAH service, they found out that VW were associated with reduction in readmissions and lower healthcare costs. Nevertheless, favorable survival was shown only for patients suffering from congestive heart failure. Tierney et al. (2021) compared an acute care, home service for elderlies with continuing care within an elderly hospitalization unit [ 28 ]. In their 1-year analysis of 505 patients, they found out that the home care was associated with higher readmission rates and higher mortality at 30 days, 3- and 6-months duration. They concluded that their results stemmed from the fact that their home-care patients’ population had a higher proportion of dependent, frail older patients. Their findings emphasize the need for thorough populations’ matching as done in our study. Leong et al. (2021) identified ten systematic reviews comparing conventional hospitalization with two models of HAH: early support discharge (ESD) and admission avoidance (AA) [ 29 ]. ESD services were found to have comparable mortality and readmissions’ rates as in-hospital stay but were associated with shorter hospitalizations. AA services showed a trend towards lower mortality, comparable or lower readmission rates. In summary, it can be concluded, from the existing literature, that medical services, at patients’ homes, are heterogenous and as they become more similar to the HAH service, they are anticipated to provide better clinical outcomes.

Sensitivity and generalizability of results

The results of our study provide promising evidence for the efficacy of tele-medicine controlled HAH care for acutely ill patients. The generalizability of these findings to other jurisdictions must account for several factors.

The first factor concerns healthcare infrastructure and technology. Regions or hospitals with well-developed telecommunication networks, electronic health record systems, and advanced remote monitoring technologies are more likely to achieve positive outcomes. Furthermore, effective tele-medicine implementation requires skilled healthcare professionals who are proficient in using digital tools and remote monitoring technologies. Variations in training, availability of skilled personnel, and access to advanced technology across different regions could impact the consistency and effectiveness of HAH programs.

The second factor is cultural and socioeconomic considerations. Patient demographics, cultural attitudes towards tele-medicine, and socioeconomic status influence the acceptance and effectiveness of HAH programs. In the current study, that was a single-center study, patients in both groups came from the same area, the Dan district in central Israel. Therefore, both groups were similar in their demographics, including financial capabilities and social status.

The third factor includes the hospital location, which can affect the feasibility and efficiency of HAH care. In regions where patients are located far from healthcare facilities, providing timely tele-medicine services may be challenging, especially if patients deteriorate and need urgent immediate care. Areas with a higher density of healthcare resources, however, may see better integration and outcomes.

Costs and resources analysis

As a retrospective analysis this study was not powered to produce a thorough cost analysis. Nevertheless, since all patients were treated by the same medical center, either in-hospital or by the HAH service, we can compare the resources needed for both treatment arms. While the net cost of one hospitalization day in-hospital is estimated at 2,830 NIS (~760 USD), the net cost of an HAH hospitalization day is only 1,660 NIS (~ 445USD) reflecting a 41.5% lowering of costs when moving from in-hospital to the HAH settings. Moreover, the HAH service personnel are engaged in other in-hospital activities and provide telemedicine services aside from the HAH service.

Future prospects of advanced technologies and artificial intelligence in the tele-health sector

The telemedicine controlled HAH service described in this manuscript included application of several advanced, designated tele-health technologies: we used a tele-health dedicated platform for monitoring and documentation of vital signs and for video calls with our patients (DATOS). We also used TytoCare system as a digital recording stethoscope and a 6-lead electrocardiography machine for patients’ self-usage. All of the above were previously validated by us in the HAH settings [ 10 – 14 ]. We did not use AI-based computing capabilities in this study. Several publications however reviewed the potential of assimilating AI into different telehealth domains: tele-diagnosis, tele-interactions, and tele-monitoring [ 30 , 31 ]. Widely described as the fourth industrial revolution, authors emphasize the potential obstacles facing whoever would assimilate AI into its telehealth services: including conflicts of patients’ privacy, transparency, and safety concerns [ 32 ]. In their recent update on ethics and governance of artificial intelligence for health, the WHO (world health organization) declared that the following principles should guide the use of AI in health: autonomy protection, promotion of human well-being, human safety and the public interest, ensuring transparency, explainability and intelligibility, foster responsibility and accountability, ensuring inclusiveness and equity and promoting AI that is responsive and sustainable [ 33 ].

This study offers compelling evidence supporting the effectiveness of home telemedicine-based hospitalization as a viable alternative to in-hospital internal medicine hospitalization. The results indicate a significantly shorter LOS without significant difference in readmission rates for both COVID-19 and non-COVID patients in home-hospitalization. Both COVID-19 and non-COVID patients receiving home hospitalization showed a significant reduction in the risk of death. The results of this study support further research in the field, preferably in the setting of prospective, controlled randomized studies.

Limitations

This study was conducted as a retrospective study at a single center. Consequently, even though we employed a thorough matching of patients’ groups, it is imperative that our results and conclusions undergo further investigation due to probable bias of patient selection. The necessity for future prospective, randomized controlled trials, where patients are randomly assigned to either home or in-hospital hospitalization, is underscored to provide more robust evidence. Several limitations were also described earlier in the “ sensitivity and generalizability of results” paragraph of the discussion.

• 1. Global Health and Healthcare Strategic Outlook: Shaping the Future of Health and Healthcare | World Economic Forum [Internet]. [cited 2024 Jan 12]. Available from: https://www.weforum.org/publications/global-health-and-healthcare-strategic-outlook-shaping-the-future-of-health-and-healthcare/
• 2. Leading problems in healthcare systems worldwide 2023 | Statista [Internet]. [cited 2024 Jan 12]. Available from: https://www.statista.com/statistics/917153/leading-problems-healthcare-systems-worldwide/
• 3. Care delivered in hospitals today will be delivered in the community and at home tomorrow | Deloitte Global [Internet]. [cited 2024 Jan 12]. Available from: https://www.deloitte.com/global/en/Industries/life-sciences-health-care/perspectives/hospital-future-without-walls.html
• View Article
• Google Scholar
• PubMed/NCBI
• 6. Bokolo Anthony Jnr. Use of Telemedicine and Virtual Care for Remote Treatment in Response to COVID-19 Pandemic. J Med Syst. 2020 Jul 1;44(7).
• 33. Health Organization W. Ethics and governance of artificial intelligence for health Guidance on large multi-modal models. 2024 [cited 2024 Jun 8]; Available from: http://apps.who.int/bookorders .

• Open access
• Published: 22 August 2024

The heterogeneous effects of COVID-19 lockdowns on crime across the world

• N. Trajtenberg   ORCID: orcid.org/0000-0002-4451-3874 1 ,
• S. Fossati 2 ,
• C. Diaz 3 ,
• A. E. Nivette 4 , 5 ,
• R. Aguilar 6 ,
• A. Ahven 7 ,
• L. Andrade 8 ,
• S. Amram 9 ,
• B. Ariel 10 ,
• M. J. Arosemena Burbano 10 ,
• R. Astolfi 11 ,
• D. Baier 12 ,
• H.-M. Bark 13 ,
• J. E. H. Beijers 5 ,
• M. Bergman 14 ,
• D. Borges 15 ,
• G. Breeztke 16 ,
• I. Cano 15 ,
• I. A. Concha Eastman 17 ,
• S. Curtis-Ham 18 ,
• R. Davenport 19 ,
• C. Droppelman 20 ,
• D. Fleitas 14 ,
• M. Gerell 21 ,
• K.-H. Jang 22 ,
• J. Kääriäinen 23 ,
• T. Lappi-Seppälä 23 ,
• W.-S. Lim 13 ,
• R. Loureiro Revilla 10 ,
• L. Mazerolle 24 ,
• C. Mendoza 25 ,
• G. Meško 26 ,
• N. Pereda 27 ,
• M. F. Peres 11 ,
• R. Poblete-Cazenave 28 ,
• E. Rojido 15 ,
• S. Rose 10 ,
• O. Sanchez de Ribera 1 ,
• R. Svensson 21 ,
• T. van der Lippe 4 ,
• J. A. M. Veldkamp 4 ,
• C. J. Vilalta Perdomo 29 ,
• R. Zahnow 24 &
• M. P. Eisner 10  

Crime Science volume  13 , Article number:  22 ( 2024 ) Cite this article

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There is a vast literature evaluating the empirical association between stay-at-home policies and crime during the COVID-19 pandemic. However, these academic efforts have primarily focused on the effects within specific cities or regions rather than adopting a cross-national comparative approach. Moreover, this body of literature not only generally lacks causal estimates but also has overlooked possible heterogeneities across different levels of stringency in mobility restrictions. This paper exploits the spatial and temporal variation of government responses to the pandemic in 45 cities across five continents to identify the causal impact of strict lockdown policies on the number of offenses reported to local police. We find that cities that implemented strict lockdowns experienced larger declines in some crime types (robbery, burglary, vehicle theft) but not others (assault, theft, homicide). This decline in crime rates attributed to more stringent policy responses represents only a small proportion of the effects documented in the literature.

Introduction

The COVID-19 pandemic involved an unprecedented change in social dynamics worldwide. The rapid diffusion of the virus and its health costs led governments to deploy policies to restrict mobility, reduce the diffusion of the virus, and avoid the collapse of health systems. The implementation of these mobility restrictions, coupled with voluntary work-from-home policies implemented by organizations and voluntary social distancing measures implemented by households, resulted in an unparalleled global reduction in human mobility, marking an extraordinarily quiet period (Lecocq et al., 2020 ). One of the key questions regarding the effects of this ‘global natural experiment’ has been its impact on crime (Boman & Mowen, 2021 ).

Mirroring research on crime and catastrophic events such as earthquakes (García Hombrados, 2020 ), studies at the intersection of COVID-19 and crime have centered on cities as the unit of analysis. These studies have employed a wide range of research designs, including interrupted time series, differences-in-differences, regression discontinuity, structural break analysis, and event studies (Koppel et al., 2023 ), documenting inconsistent effects across different offenses. For example, research in the US, Canada, the UK, Australia, Mexico, India, New Zealand, Sweden, and China consistently documented a significant decrease in the reported incidents of property crimes, such as robbery, theft, or burglary (Abrams, 2021 ; Andresen & Hodgkinson, 2020 ; Ashby, 2020 ; Balmori de la Miyar et al., 2020 ; Chen et al., 2023 ; Cheung & Gunby, 2021 ; Felson et al., 2020 ; Gerrell et al., 2020 ; Halford et al., 2020 ; Koppel et al., 2023 ; Langton et al., 2021 ; Payne et al., 2021 ; Poblete-Cazenave, 2020 ; Vilalta et al., 2023 ). On the other hand, other property crimes, such as vehicle theft, showed more varied outcomes. Reductions were reported for Australia, the US, China and UK (Andresen & Hodgkinson, 2020 ; Chen et al., 2023 ; Halford et al., 2020 ; Koppel et al., 2023 ; Mohler et al., 2020 ), while other studies documented no effects or even an increase for the US and Canada (Ashby, 2020 ; Hodgkinson & Andresen, 2020 ; Meyer et al., 2022 ). The relationship between the pandemic restrictions and violent crimes is less clear. While some studies focusing on cities in the US, UK, Australia, Sweden, India, Mexico, and Peru show significant reductions in reported assaults or homicides (Abrams, 2021 ; Calderon-Anyosa & Kaufman, 2021 ; Gerrell et al., 2020 ; Halford et al., 2020 ; Payne et al., 2021 ; Poblete-Cazenave, 2020 ; Vilalta et al., 2023 ), other studies in Australia, the US, and Mexico show no significant effects (Balmori de la Miyar et al., 2020 ; Campidelli et al., 2020a ; Koppel et al., 2023 ; Lopez & Rosenfeld, 2021 ; Meyer et al., 2022 ; Payne et al., 2020 ).

Since most of this evidence is based on single-city studies or, at best, studies that compare different cities within one country (e.g., Abrams, 2021 ; Ashby, 2020 ; Meyer et al., 2022 ) or a few countries (Cecatto et al., 2021 ), inconsistency across studies is not surprising given the different characteristics of cities, type of stay-at-home government restrictions, voluntary measures implemented by organizations and households, and period of analysis. The only study with broad international coverage is Nivette et al. ( 2021a ), which included 23 cities across the Americas, Europe, the Middle East, and Asia and documented an average reduction of 37% in reported offenses following stay-at-home government restrictions. Although property crimes (i.e., theft, vehicle theft, burglary, and robbery) exhibited a significant reduction, violent crimes showed a mixed picture with a reduction of assaults, while homicides showed no effects. In addition, Nivette et al. ( 2021a ) documented heterogeneity in crime reduction across locations, with the largest crime drops in cities that applied stricter lockdowns. These results are consistent with a recent systematic review, which indicated that most crimes exhibited a significant reduction following COVID-19 restrictions, except for homicides (Hoeboer et al., 2024 ).

Despite the accumulated empirical evidence on the relationship between the COVID-19 pandemic and crime across different types, one of the limitations of this literature is the focus on the generic role of the pandemic and associated measures. Considering the widespread impact of the pandemic, most studies compare current trends with a counterfactual scenario representing the expected crime rate based on pre-pandemic periods (Abrams, 2021 ; Hodkinson & Andresen, 2020 ; Nivette et al., 2021a ). Utilizing cities’ pre-pandemic trends as control groups implies the comparison of cities affected by the pandemic with those not impacted by the pandemic. This entails assessing the effects of a general ‘treatment,’ encompassing various and distinct government restrictions and stay-at-home policies associated with the pandemic and considering individual and corporate responses, which may be partially independent of government policies. Exceptionally, some studies have exploited heterogeneities across neighborhoods to identify a potential causal effect on crime at a local level. For example, when initial restrictions were relaxed in Bihar (India), crime rates increased. Still, the rise was less pronounced in areas with more stringent restrictions compared to those with less severe measures, and notably, this uptick did not extend to violent crimes (Poblete-Cazenave, 2020 ). Instead, evidence from London (UK) shows that easing national lockdown measures diminished the effect of stringent lockdowns across all property and violent crimes (Neanidis & Rana, 2023 ). Evidence from Oslo (Norway) suggests that when general COVID-19 restrictions were accompanied by the closure of bars and pubs, there were additional significant reductions in theft, violent crimes, vandalism, and fraud (Gerrell et al., 2022 ). However, the scarcity of integrated international data sets (Boman & Mowen, 2021 ; Nivette, 2021 ) means we lack causal estimates of the impact of specific types of government policies on crime at a global level.

Consequently, in this paper, we investigate how different policies implemented by local governments during the COVID-19 pandemic affected crime across cities. The goal is to identify the causal impact of strict lockdown policies on the number of offenses reported to local police. More specifically, our main research question is: What is the cross-national impact on crime of strict lockdown policies that require citizens not to leave their homes? To answer this question, we first analyze the spatial and temporal variation of government responses to the pandemic in 45 cities throughout the year 2020. We define strict lockdown conditions as instances where governments require citizens not to leave the house with no or minimal exceptions. Next, we apply a generalized synthetic control approach, which involves building a weighted combination of control groups of all the cities in those periods without strict lockdown. Thus, only cities that implemented ‘strict lockdowns’ are considered ‘treated,’ some of them intermittently, some of them permanently during the period of study. This empirical strategy allows us to isolate the effect of strict lockdowns on crime by accounting for the effects of other stay-at-home policies in cities with less stringent restrictions. We conclude by discussing the policy implications of our results for implementing crime prevention policies that may compromise individuals’ freedom of movement.

Data and methods

The outcome variable.

The outcome variable is the number of crime incidents reported to police each month in each of the 45 cities in our sample between January 2018 and December 2020 for six crime types: assault, theft, burglary, robbery, vehicle theft, and homicide. Footnote 1 The cities were selected to maximize geographical coverage. The integration, aggregation, and comparison of crimes across these units were based on the International Classification of Crime for Statistical Purposes (Bisogno et al., 2015 ). In some cities, the six crime categories were not available or did not fit our classification. For example, vehicle theft was not considered a separate category from theft in some cities (e.g., Zurich), and burglary is not clearly distinguished from another type of property crime in other cases (e.g., Montevideo). In other cities, crime counts were available but not for the relevant period of study (e.g., Tel Aviv). Additionally, some cities lacked enough monthly cases for some crimes, typically homicide (e.g., Ljubljana). As a result, some cities could not be included in some of the analyses (see Table A2 of the Supplementary Materials). Finally, due to the surge in violence following the George Floyd incident on May 25, 2020, our main results are based on data before May 2020 for the 15 US cities in our study. However, we report the results using all US data in section E of the Supplementary Materials.

The date of the time series refers to the date when the offense presumably occurred, as recorded by the police. In cases where this information was not available, the reporting date was used (e.g., Mexico City). Since not all reported crimes were investigated, the number may under-represent the volume of crime reported to the police. Additionally, for most cities, the time series starts on 1 January 2018 or 2019 and ends on 31 December 2020. Time series information and available crime categories for each city are presented in Table A2.

To compare the changes in crime trends following the onset of the COVID-19 pandemic and the implementation of stay-at-home restrictions, we calculated monthly indexes for each city and crime type. The indexes were constructed such that the 2019 average equals 100 for each time series. Figure  1 shows the indexes for all cities in a single graph, with the average trend for each crime type highlighted in orange. We used monthly time series to minimize suppression since, in some cities, the frequency of crimes per day was almost zero. This occurred most often for homicide.

Monthly crime indexes. The indexes are constructed such that their 2019 average equals 100. The average trend for each crime type is highlighted in orange

Event study design

For each crime, we have an outcome matrix with typical element \({y}_{it}\) with 45 columns corresponding to cities (N = 45) and 36 rows corresponding to the months from January 2018 to December 2020 (T = 36). We first estimated the average effect of the COVID-19 pandemic on crime incidents using a two-way fixed effects (static) regression given by:

where \({y}_{it}\) is the monthly index of crime incidents for a given type of crime in city i on month t . The (first) treatment variable \({D}_{it}^{1}\) is a dummy variable that takes the value of 1 during the period starting in March 2020 and 0 in the period preceding it. In this case, the treatment variable captures all stay-at-home restrictions implemented by governments, voluntary work-from-home policies implemented by employers, voluntary social distancing implemented by individuals, and any other change in behavior that can be attributed to the COVID-19 pandemic. In this specification, the coefficient \(\delta\) represents the average percentage change in crime incidents (relative to the 2019 average) after the onset of the COVID-19 pandemic in March 2020.

For certain crimes, patterns have been found to increase periodically. As a result, we used weather covariates and fixed effects to control for seasonal and long-term trends. The vector \({w}_{it}\) includes weather covariates (monthly average rain and temperature for each city), \({\alpha }_{i}\) denotes city fixed effects to control for factors that vary across cities but not across time, \({\theta }_{t}\) denotes month fixed effects to account for monthly seasonality, and \({\gamma }_{t}\) denotes year fixed effects to account for time trends in the data.

Next, we estimated the average effect of the COVID-19 pandemic on crime incidents for each pandemic month using a (dynamic) regression given by:

where \({D}_{it}^{j}\) are treatment dummy variables for each of the pandemic months (March to December) of 2020. In this specification, the coefficient \({\delta }_{j}\) represents the average percentage change in crime incidents for month j (relative to the 2019 average).

Matrix completion design

Our main goal is to evaluate how the implementation of strict lockdowns impacted crime trends in 2020. Let \({D}_{it}^{2}\) be the (second) treatment variable equal to 1 if city i was in strict lockdown during month t . The treatment variable has the same dimension as the outcome matrix and takes the value of 1 when strict lockdown restrictions (not just recommendations to stay-at-home) were in place in city i , while 0 represents the period before or following the implementation of strict lockdown restrictions in that city. Since strict lockdowns were implemented intermittently, the treatment adoption exhibits an on–off pattern as the date cities enter/exit a strict lockdown varies across cities.

The date lockdown restrictions were implemented across cities is not always clear. As a result, we relied on a sub-index from the Oxford COVID-19 Government Response Tracker (OxCGRT). The sub-index C6 monitors “orders to shelter-in-place and otherwise confine to the home,” takes ordinal values {0, 1, 2, 3}, and is reported daily. The index takes the value 0 if no measures are in place, 1 when “recommend not leaving house,” 2 when “require not leaving house with exceptions,” and 3 when “require not leaving house with minimal exceptions” (Hale et al., 2020 ). The sub-index C6 is plotted for each of the 45 cities in the sample in Figure B1 of the Supplementary Materials. To construct our (second) treatment variable, a city is considered under strict lockdown (i.e., the second treatment variable is 1) when the sub-index C6 takes the values 2 or 3 (“require not leaving house”). When the sub-index C6 takes the value 0 or 1, the city is considered untreated (i.e., the second treatment variable is 0). The second treatment variable is plotted in Figure B2 of the Supplementary Materials. Footnote 2

Under the potential outcomes framework (Rubin, 1974 ), for each city i and month t , there are a pair of potential outcomes, \({y}_{it}^{1}\) and \({y}_{it}^{0}\) , that correspond to the potential outcomes under treatment (strict lockdown) and control condition (stay-at-home recommendation but no strict lockdown), respectively. To assess the effect of strict lockdowns for each crime type, we need to estimate a counterfactual matrix \({y}_{it}^{0}\) in which elements represent the monthly index of crime incidents for cities that are not in strict lockdown (untreated cities). When a city is in strict lockdown ( \({D}_{it}^{2}=1\) ), the outcome variable is removed from the matrix of outcomes, and the observation is treated as missing. The objective is to “complete” the matrix of outcomes under the assumption that no lockdown has taken place (Liu et al., 2024 ). The causal quantity of interest is the average treatment effect on the treated (ATT).

In this paper, we used the matrix completion (MC) counterfactual estimator of Athey et al. ( 2021 ) and Liu et al. ( 2024 ). The MC estimator is given by:

where \({y}_{it}^{0}\) is the monthly index of crime incidents for a given type of crime for untreated cities, \({\alpha }_{i}\) and \({\gamma }_{t}\) denote unit (city) and time fixed effects, \(L\) is a matrix to be estimated with typical element \({L}_{it}\) (see Liu et al., 2024 ), and \({w}_{it}\) are weather covariates. Athey et al. ( 2021 ) provide an algorithm that uses regularization to estimate the model and impute the missing values in the counterfactual matrix. This estimator generates a counterfactual outcome \({y}_{it}^{0}\) for each treated observation and, as a result, we can estimate the individual treatment effect \({\delta }_{it}\) as the difference between the estimated \({y}_{it}^{0}\) and the observed \({y}_{it}\) , \(\widehat{{\delta }_{it}}={y}_{it}-{y}_{it}^{0}\) . Next, we can compute ATTs of interest as averages of the \(\widehat{{\delta }_{it}}\) for a subset of the observations under treatment. For example, we can compute the overall ATT by calculating the average for all treated observations. Alternatively, we can compute the ATT for each of the pandemic months separately or the ATT for each month relative to the beginning of a lockdown, etc. Standard errors and confidence intervals can be obtained using 1000 block bootstraps at the city level and jackknife (leave-one-out) methods, as in Liu et al. ( 2024 ).

If the treatment (strict lockdown) impacts crime, the observed frequency of monthly crime rates should be lower in the treated periods than in the counterfactual estimates. Therefore, rather than comparing the pandemic’s impact on crime in each city to what would have occurred without it (utilizing pre-pandemic crime trends as a baseline), we are examining the effect of strict lockdown measures on crime rates in cities like Mexico or London. To achieve this, we use cities such as Malmö or Stockholm, where no strict lockdown was imposed during the pandemic, as control groups. Thus, our ATT analysis yields an estimation of the reduction in crime attributable to the enforcement of stringent lockdown measures while controlling for the effect of the pandemic in cities that did not enforce such measures yet still experienced decreased mobility due to stay-at-home government policies and behavioral adjustments by organizations and individuals.

The impact of the COVID-19 pandemic on crime

We begin by evaluating the overall impact of the COVID-19 pandemic on crime. Table 1 reports the average percentage change in crime incidents for each crime type after the onset of the COVID-19 pandemic in March 2020 from Eq. ( 1 ). Our results show that, on average, across all the cities, there was a substantial drop in crime relative to the 2019 average. This effect is found to be particularly large and statistically significant for theft (31.5% drop in monthly counts), robbery (28.2% drop), burglary (20.4% drop), assault (20.1% drop), and vehicle theft (18.8% drop). In contrast, the effect on homicide was smaller (10.3% drop) and not statistically significant.

Figure  2 plots the average percentage change in crime incidents for each month after March 2020 obtained from Eq. ( 2 ). Our results show there was a large drop in crime during the first few months of the pandemic (mainly March, April, and May), followed by a moderate bounce back during the northern hemisphere summer of 2020. The drop in crime settled around 25% for burglary, robbery, and vehicle theft, and close to 30% for theft. The drop was smaller for assault, settling around a 10% drop. Finally, we observe a small and persistent drop in homicide that is not statistically significant. Overall, these estimates are consistent with those documented in the literature, although somewhat smaller in comparison to the reductions in crime reported in Nivette et al. ( 2021a ).

Average effect of the COVID-19 pandemic on crime by month

The impact of strict lockdown on crime

To evaluate the impact of strict lockdowns on crime, we need to identify the periods in which strict lockdown restrictions, not just stay-at-home recommendations, were in place in each city. In addition, we need to control for changes in behavior that can be attributed to the pandemic (e.g., voluntary work-from-home policies) but not to a strict lockdown mandated by governments. Figure B2 of the Supplementary Materials shows the temporal and spatial variation of strict lockdown for all cities and periods in our sample. Before March 2020, all cities were considered untreated. After March 2020, cities that imposed strict lockdowns were considered treated, some intermittently and some permanently. For example, Mendoza and Lima were treated during all the study periods because they imposed strict lockdowns from March until the end of 2020. In contrast, Stockholm and Malmö were control cases for the whole period because no strict lockdown was imposed. London was considered under treatment during the first three months and the last two months of 2020 but was considered a control case during spring and all summer of the northern hemisphere. Table C1 of the Supplementary Materials shows that being under strict lockdown (i.e., when the second treatment variable is equal to 1) was associated with a substantial reduction in mobility, in addition to the reduction observed in cities without strict lockdown (i.e., the control cities). For example, cities under strict lockdown experienced a drop in retail and recreation mobility that is 89.8% larger than what was observed in the control cities. Similarly, the drop was 58.8% larger for transit mobility and 64.7% larger for workplace mobility.

Table 2 presents the estimated ATT of strict lockdown using the MC counterfactual estimator. Our results show that, on average, across all the cities and all of 2020 months, the impact of strict lockdowns was negative for all crimes relative to the cases without strict lockdowns. This effect was found to be particularly large and statistically significant in the case of robbery (19.1% drop in monthly counts), burglary (14.9% drop), and vehicle theft (11.9% drop). However, the impact of strict lockdowns on assault (3.5% drop) and theft (5.4% drop) was small and not statistically significant. Finally, the effect of strict lockdowns on homicide was large (12.9% drop) but not statistically significant. Nevertheless, the analysis of homicide included fewer cities due to missing observations and a lack of sufficient volume of monthly rates (e.g., only 25 cities were included in the analysis of homicides). Footnote 3 Footnote 4

Next, we estimate the dynamic ATT of strict lockdown on crime for each month, relative to cities that did not experience a strict lockdown in that month (i.e., the difference between the estimated effect of the COVID-19 pandemic on crime for the treated and untreated each month). Footnote 5 Visual inspection of Fig.  3 reveals that the ATTs of strict lockdown on crime were observed only after May 2020. In addition, there was substantial heterogeneity across the different waves of the pandemic. For example, the monthly ATT of strict lockdown on assault exhibited a U-shape with a significant reduction in the first months of the pandemic (an additional 25% drop relative to cities with less stringent stay-at-home policies). However, the effect gradually ceased to be significant, with no effect after August. Robbery exhibited a similar trend with a more substantial and persistent initial reduction (a 30% drop) that gradually ceased to be statistically significant after the June/August period, except for a large drop in October. Burglary, in contrast, exhibited a W-shaped pattern with two drops in the period, a significant reduction in the first wave of the pandemic (a 30% drop) and a second large reduction, though smaller, during the second wave (a 20% drop). The effect of strict lockdown on theft and vehicle theft across time was less clear, and although some specific months exhibit statistically significant reductions, most months showed non-significant differences. Likewise, there was no apparent effect of strict lockdown on homicide across time due to the lack of sufficient volumes of cases.

Average treatment effect on the treated (ATT) of strict lockdown on crime by month. Cities under strict lockdown are considered treated

A follow-up question is: what is the impact of strict lockdown on crime as consecutive months of lockdown accumulate? Fig.  4 shows the ATT of strict lockdown on crime relative to the start of the strict lockdown. This was obtained by computing the ATT for treated observations that correspond to any first month of strict lockdown, then we compute the ATT for treated observations that correspond to any second consecutive month of strict lockdown, and so on. Our results show mostly non-significant reductions in crime in the first two months of strict lockdown relative to cities with less stringent mobility restrictions. However, as months of lockdown accumulated, there was an increasingly significant reduction in crime rates for burglary, robbery, and assault. In contrast, there was little effect on theft and vehicle theft, and no significant effect on homicide.

Average treatment effect on the treated (ATT) of strict lockdown on crime relative to the start of the strict lockdown

Nevertheless, these results should be interpreted with caution because the analysis was based on a reduced sample. While almost all cities in the sample (except for Stockholm and Malmo) experienced at least one month of strict lockdown, the number of cities under strict lockdown for two or more consecutive months was substantially smaller. For example, the impact of strict lockdowns on robbery that involves four consecutive months was estimated using only ten cities. Thus, as we consider the effect of longer lockdowns, we have fewer cities, and rejections of the null hypothesis of no effect are more likely to be driven by the idiosyncratic effects of the cities remaining in the sample. Moreover, we only considered the cumulative effect of consecutive periods of treatment. If the treatment (strict lockdown) was interrupted by periods of no lockdown, the new period of lockdown was considered as a new first month of treatment. For example, London had two strict lockdown periods: one lasting three months in the northern hemisphere spring and the second one of 2 months in winter.

Our findings show that cities under strict lockdown experienced substantial declines in robberies, burglaries, and vehicle thefts, compared to cities under less stringent stay-at-home orders. However, when comparing cities with strict and non-strict lockdowns, we found no significant effects on assaults, thefts, and homicides. Non-economic violent crimes, such as assaults and homicides, are often situational and linked to spontaneous conflicts in public settings associated with leisure activities (Wilcox & Cullen, 2018 ). The initial stages of the COVID-19 pandemic involved the closure of the night-time economy and the cessation of public situational contexts (e.g., pubs, bars, and other outlets) where such violent frictions are more likely to occur (Ejrnæs & Scherg, 2022 ; Gerrell et al., 2022 ). Thus, when strict lockdowns were implemented, the opportunities for these types of violent crimes were already significantly reduced. It is also possible that some shift took place from violent frictions in public settings to more private settings. There is evidence of an increase in reports of domestic incidents (Piquero et al., 2021 ), particularly by current partners and not by former partners (Ivandić et al., 2020 ). Moreover, a portion of homicides occurs within the context of organized crime activities, which was less impacted by the stringency of health measures (Hoeber et al., 2024 ).

The results regarding theft reports are puzzling. In fact, this economically motivated street crime exhibits one of the most consistent findings across the COVID-19 literature (Hoeboer et al., 2024 ). One possibility is that theft showed stability during strict lockdown because the reduction of criminal opportunities due to changes in routine activities had already taken place in the first weeks of the pandemic, where people had significantly decreased their interactions in the public sphere (Felson et al., 2020 ). Additionally, this new context, with fewer potential victims due to reduced interpersonal contacts in the streets coupled with an increase of capable guardianship at homes, might have led robbers and burglars to switch to thefts. These findings are consistent with previous evidence on “functional displacement” to other crimes, particularly with strongly motivated offenders, when there is an expectation of reducing risks, and usually toward less serious crimes (Rossmo & Summers, 2021 ; see also Johnson et al., 2014 ). Theft is a very generic category, and more fine-graded data would allow us to understand how this displacement might be associated with some specific categories of theft like shoplifting, bicycle theft, or theft of items left outside houses in porches or garages. Yet, more research is needed to understand why strict lockdowns might have had different effects on property crimes with economic motivations and which contextual and specific mechanisms explain these differences.

It is hard to know if changes in crime rates during strict lockdowns are attributable to mechanisms distinct from alterations in criminal opportunities. Although the literature mentions opportunities and strains as potential explanatory mechanisms (Campedelli et al., 2020a ; Stickle & Felson, 2020 ), research has focused mainly on the role of opportunities, with few exceptions showing how changes in interpersonal violence and violent property crimes during the pandemic can be partially explained by geographical differences associated with poverty, unemployment, and inequality (Andresen & Hodkinson, 2020 ; Campedelli et al., 2020b ). Strains are more likely to have long-term effects on crime (Eisner & Nivette, 2020 ), as government measures are relaxed, and routine changes become less relevant (Payne et al., 2021 ). For example, research conducted in the US suggests that the surge in violent crimes during reopening phases following lockdowns may be attributed to a combination of heightened opportunities and the accumulation of strains (Ridell et al., 2021 ). Nevertheless, empirical support for the role of strains in the COVID-19 literature is weak, and its relevance as an explanatory mechanism has been challenged, notably when it comes to the prediction of crimes such as domestic violence (Aebi et al., 2021 ; Hodgkinson et al., 2023 ). Our analysis does not reveal significant differences between more violent situational crimes (like assaults) and economically motivated crimes (such as theft), even after several consecutive months of lockdown. However, our findings should be taken with caution, given the limitations of our analysis (e.g., the exclusion of US cities after May 2020 and a limited number of cases with extended strict lockdowns).

Our results show that the additional reduction in crime rates due to strict lockdowns was small and stronger mobility restrictions did not translate into substantially larger drops in crime. In other words, the relationship between mobility and crime does not appear to be linear as further reductions in mobility had marginal effects on crime. This result suggests that crime reductions during the pandemic were not only driven by local sanitary restrictions implemented by governments but also by people’s preventive behavior and organizations’ policies (e.g., flexible work-from-home conditions) (Barrero et al., 2021 ). Thus, when a strict lockdown was imposed, both people and organizations had already reacted, altering routine activities and crime opportunities (Stickle & Felson, 2020 ). In simpler terms, strict lockdowns did not substantially change the number of potential victims on the streets or the occupancy levels in households despite reducing mobility. These had already decreased significantly beyond the initial mobility decline prompted by the initial guidelines, as well as the precautionary measures taken by organizations and individuals. Thus, stricter lockdowns had only a marginal effect, as the new scenario did not significantly increase the difficulties or costs of finding criminal targets (Nagin, 2013 ).

Our findings carry policy implications. This study suggests that most of the crime reduction took place without the need for a costly and extensive ‘massive social incapacitation’ of citizens by the government (strict lockdown), forcing them to have a ‘house arrest experience’ (Baker, 2020 ). While the estimates in Table  2 show a negative average effect of strict lockdowns for all crimes (relative to cases without strict lockdowns), they indicate a diminishing or null effect when compared to the findings in Table  1 . This implies that achieving crime reduction can rely more on citizens’ autoregulation and less on sacrificing citizens’ freedom of movement. During the COVID-19 pandemic, policymakers explored alternatives that balance public health concerns and preserve individual liberties. Similarly, effective crime reductions can be attained through measures that are less restrictive of citizens’ freedom of circulation.

This study is not without limitations. First, many cities in the sample have a very low frequency of homicides. Although our study finds no significant effects on homicides, the low frequency of these incidents presents challenges in terms of statistical inference when determining how these crimes were affected by strict lockdowns. This is a common limitation in natural disaster studies, which focus on aggregate measures of violent crimes rather than homicides (Doucet & Lee, 2015 ). Second, our study is limited by the use of police records. This not only presents the challenge of comparing and harmonizing crime categories across different legal frameworks in various countries (Aebi, 2010 ) but also involves issues related to the reporting, recording, and publishing of data, which vary significantly across crime categories (Ashby et al., 2022 ; Buil-Gil et al., 2021 ; Xie & Baumer, 2019 ). Particularly problematic is that selection biases not only influence how victims report crimes and how police officers record them, but these processes are also heterogeneous across units of analysis (Estienne & Morabito, 2016 ; Torrente et al., 2017 ). Additionally, the pandemic might have further exacerbated bias in crime measurement. For example, underreporting might have occurred due to victims and police fearing contagion. At the same time, under-recording could have resulted from reduced police department capacity to register, respond to calls, and patrol (Wallace et al., 2021 ). Nevertheless, some studies have shown that part of the crime drop is not an artifact of underreporting by providing robustness checks by contrasting trends of different types of crimes before and after the pandemic (Abrams, 2021 ), or by triangulating police crime records with victimization surveys (Perez-Vincent et al., 2021 ). Finally, our sample has a limited geographic variance which affects the external validity of our findings. Although the sample almost doubled the number of cities in relation to Nivette et al. ( 2021a ) and included relevant cities from South America and the Caribbean, there is still an overrepresentation of North America and Europe. One challenge is to incorporate more cases from underrepresented regions and have a more representative sample in terms of low- and middle-income non-western societies (Boman & Mowen, 2021 ; Eisner, 2023 ) with more variability of crime indicators, correlates of crime, but also in terms of validity of their police crime statistics (Mendlein, 2021 ; Rogers & Pridemore, 2017 ).

Conclusions

During the COVID-19 pandemic, governments implemented a variety of stay-at-home policies to reduce mobility and prevent the spread of the virus. Cities under strict lockdowns across North America, South America, Europe, Asia, and Oceania experienced larger declines in property crimes, such as robbery, burglary, and vehicle theft, when compared to cities under less stringent stay-at-home policies. However, more stringent stay-at-home policies did not seem to have a more significant effect than less stringent policies on assault, theft, or homicide. The reduction in crime rates attributed to these more stringent policies represents only a small proportion of the overall effect of the pandemic on crime. Relevant lessons can be extracted regarding the necessity of implementing stringent measures on citizens' rights and freedom of movement to reduce crime.

Availability of data and materials

Data and codes to conduct analysis in this study are available on the website of one of the authors: www.carlosddiaz.com .

See Table A1 of the Supplementary Materials. See also the supplementary materials of the previous study (Nivette et al., 2021b ).

A city was considered treated if at least one day of the month the city was under strict lockdown. While length of strict lockdown varies significantly across the sample, most lockdowns lasted longer than 2 weeks (see Figure B2).

Placebo test results are reported in Table D1 of the Supplementary Materials. Treatment is introduced four months before the actual treatment (strict lockdown) creating an in-time placebo period. We then estimate the ATT for the placebo period using the MC estimator. We find no evidence for a lockdown effect on crime for any of the crime types considered (all p-values > 0.05).

The results using all US data are reported in section E of the Supplementary Materials. Our results for assault, burglary, robbery, theft, and vehicle theft remain mostly unchanged (see Figures E1–E3). In contrast, we now observe a large and statistically significant reduction in homicide. However, this effect was not due to a drop in homicide in the treated but due to a large increase in homicide in the control cities, which included all the US cities. For a discussion of a potential Minneapolis effect due to the Floyd case see Ratcliffe & Taylor ( 2023 ).

Figure B3 of the Supplementary Materials plots the estimated effect of the COVID-19 pandemic on crime for the two groups of cities considered (treated and untreated) for each month. The results show that cities under strict lockdown (the treated) had systematically larger reductions in crime across all types of crime compared to cities with less stringent stay-at-home policies (the untreated).

Abrams, D. S. (2021). COVID and crime: An early empirical look. Journal of Public Economics, 194 , 104344.

Article   Google Scholar  

Aebi, M. F. (2010). Methodological issues in the comparison of police-recorded crime rates. International handbook of criminology (pp. 237–254). London: Routledge.

Google Scholar  

Aebi, M. F., Molnar, L., & Baquerizas, F. (2021). Against all odds, femicide did not increase during the first year of the COVID-19 pandemic: Evidence from six Spanish-speaking countries. Journal of Contemporary Criminal Justice, 37 (4), 615–644.

Andresen, M. A., & Hodgkinson, T. (2020). Somehow, I always end up alone: COVID-19, social isolation and crime in Queensland, Australia. Crime Science, 9 (1), 25.

Ashby, M. (2020). Initial evidence on the relationship between the coronavirus pandemic and crime in the United States. Crime Science., 9 , 6.

Ashby, M., Bal, M. G., Croci, G., Fuller, A., Mantl, N., & Youngsub, L. (2022). Benchmarking crime in London against other global cities. Center for Global City Policing, December, Report. Retrieved from https://discovery.ucl.ac.uk/id/eprint/10183479/1/global_cities_crime_benchmarking_2021.pdf . Accessed 31 Jun 2024.

Athey, S., Bayati, M., Doudchenko, N., Imbens, G., & Khosravi, K. (2021). Matrix completion methods for causal panel data models. Journal of the American Statistical Association, 116 (536), 1716–1730.

Baker, E. (2020). The crisis that changed everything: Reflections of and reflections on COVID-19. European Journal of Crime, Criminal Law and Criminal Justice, 28 (4), 311–331.

Balmori de la Miyar, J. R. B., Hoehn-Velasco, L., & Silverio-Murillo, A. (2020). Druglords don’t stay at home: COVID-19 pandemic and crime patterns in Mexico City.  Journal of Criminal Justice 101745.

Barrero, J. M., Bloom, N., & Davis, S. J. (2021).  Why working from home will stick  (No. w28731). National Bureau of Economic Research.

Bisogno, E., Dawson-Faber, J., & Jandl, M. (2015). The International Classification of Crime for Statistical Purposes: A new instrument to improve comparative criminological research. European Journal of Criminology, 12 , 535–550.

Boman, J. H., & Mowen, T. J. (2021). Global crime trends during COVID-19. Nature Human Behaviour, 5 , 821822.

Buil-Gil, D., Medina, J., & Shlomo, N. (2021). Measuring the dark figure of crime in geographic areas. Small area estimation from the Crime Survey for England and Wales. British Journal of Criminology, 61 (2), 364–388.

Calderon-Anyosa, R., & Kaufman, J. S. (2021). Impact of COVID-19 lockdown policy on homicide, suicide, and motor vehicle deaths in Peru. Preventive Medicine, 143 , 106331.

Campedelli, G. M., Aziani, A., & Favarin, S. (2020a). Exploring the Immediate Effects of COVID-19 Containment Policies on Crime: An Empirical Analysis of the Short-Term Aftermath in Los Angeles. American Journal of Criminal Justice, 46 , 704–727.

Campedelli, G. M., Favarin, S., Aziani, A., & Piquero, A. R. (2020b). Disentangling community-level changes in crime trends during the COVID-19 pandemic in Chicago. Crime Science, 9 (1), 1–18.

Ceccato, V., Kahn, T., Herrmann, C., & Östlund, A. (2022). Pandemic restrictions and spatiotemporal crime patterns in New York, São Paulo, and Stockholm. Journal of Contemporary Criminal Justice , 38 (1), 120–149.

Chen, P., Kurland, J., Piquero, A. R., & Borrion, H. (2023). Measuring the impact of the COVID-19 lockdown on crime in a medium-sized city in China. Journal of Experimental Criminology, 9 , 1–28.

Cheung, L. & Gunby, P. (2021). Crime and mobility during the COVID-19 lockdown: a preliminary empirical exploration. New Zealand Economic Pa pers, p 1–8.

Doucet, J. M., & Lee, M. R. (2015). Civic communities and urban violence. Social Science Research, 52 (2015), 303–316.

Eisner, M., & Nivette, A. (2020). Violence and the pandemic: Urgent questions for research . Harry Frank Guggenheim Foundation.

Eisner, M. (2023). Towards a global comparative criminology. In A. Liebling, S. Maruna, & L. McAra (Eds.), The oxford handbook of criminology (pp. 75–98). Oxford University Press.

Chapter   Google Scholar  

Ejrnæs, A., & Scherg, R. H. (2022). Nightlife activity and crime: The impact of COVID-19 related nightlife restrictions on violent crime. Journal of Criminal Justice, 79 , 101884.

Estienne, E., & Morabito, M. (2016). Understanding differences in crime reporting practices: A comparative approach. International Journal of Comparative and Applied Criminal Justice, 40 (2), 123–143.

Felson, M., Jiang, S., & Xu, Y. (2020). Routine activity effects of the COVID-19 pandemic on burglary in Detroit. Crime Science, 9 , 10.

García Hombrados, J. (2020). The lasting effects of natural disasters on property crime: Evidence from the 2010 Chilean earthquake. Journal of Economic Behavior and Organization, 175 , 114–154.

Gerrell, M., Kardell, J. & Kindgren, J. (2020) Minor COVID-19 association with crime in Sweden. Crime Science 9(1).

Gerell, M., Allvin, A., Frith, M., & Skardhamar, T. (2022). COVID-19 restrictions, pub closures, and crime in Oslo, Norway. Nordic Journal of Criminology, 23 (2), 136–155.

Hale, T., Webster, S., Petherick, A., Phillips, T., & Kira, B. (2020). Oxford COVID-19 Government Response Tracker, Blavatnik School of Government.

Halford, E., Dixon, A., Farrell, G., Malleson, N., & Tilley, N. (2020). Crime and coronavirus: Social distancing, lockdown, and the mobility elasticity of crime. Crime Science, 9 (1), 1–12.

Hodgkinson, T., & Andresen, M. A. (2020). Show me a man or a woman alone and I’ll show you a saint: Changes in the frequency of criminal incidents during the COVID-19 pandemic. Journal of Criminal Justice, 69 , 101706.

Hodgkinson, S., Dixon, A., Halford, E., & Farrell, G. (2023). Domestic abuse in the Covid-19 pandemic: Measures designed to overcome common limitations of trend measurement. Crime Science, 12 (1), 12.

Hoeboer, C. M., Kitselaar, W. M., Henrich, J. F., Miedzobrodzka, E. J., Wohlstetter, B., Giebels, E., Meynen, E. W., Kruisbergen, M., Kempes, M., de Olff, M., & de Kogel, C. H. (2024). The impact of COVID-19 on crime: A systematic review. American Journal of Criminal Justice, 49 , 274–303.

Ivandic, R., Kirchmaier, T.,& Linton, B. (2020). Changing patterns of domestic abuse during COVID-19 lockdown. SSRN Scholarly Paper ID 3686873.

Johnson, S. D., Guerette, R. T., & Bowers, K. (2014). Crime displacement: What we know, what we don’t know, and what it means for crime reduction. Journal of Experimental Criminology, 10 , 549–571.

Koppel, S., Capellan, J. A., & Sharp, J. (2023). Disentangling the impact of COVID-19: An interrupted time series analysis of crime in New York City. American Journal of Criminal Justice, 48 (2), 368–394.

Langton, S., Dixon, A. & Farrell, G. (2021). Six months in: pandemic crime trends in England and Wales. Crime Science 10 (6).

Lecocq, T., Hicks, S. P., Van Noten, K., Diaz, J., et al. (2020). Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures. Science, 369 (6509), 1338–1343.

Liu, L., Wang, Y., & Xu, Y. (2024). A practical guide to counterfactual estimators for causal inference with time-series cross-sectional data. American Journal of Political Science, 68 (1), 160–176.

Lopez, E., & Rosenfeld, R. (2021). Crime, quarantine, and the U.S. coronavirus pandemic. Criminology and Public Policy, 20 (3), 401–422.

Mendlein, A. K. (2021). International differences in crime reporting: A multilevel exploration of burglary reporting in 35 countries. International Criminal Justice Review , 31 (2), 140–160.

Mohler, G., Bertozzi, A. L., Carter, J., Short, M. B., Sledge, D., Tita, G. E., Uchida, C. D., & Brantingham, P. J. (2020). Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis. Journal of Criminal Justice, 68 , 101692.

Meyer, M., Hassafy, A., Lewis, G., Shrestha, P., Haviland, A. M., & Nagin, D. S. (2022). Changes in crime rates during the COVID-19 pandemic. Statistics and Public Policy, 9 , 97.

Nagin, D. S. (2013). Deterrence in the twenty-first century. Crime and Justice, 42 (1), 199–263.

Neanidis, K. C., & Rana, M. P. (2023). Crime in the era of COVID-19: Evidence from England. Journal of Reginal Science, 63 (5), 1100–1130.

Nivette, A. E. (2021). Exploring the availability and potential of international data for criminological study. Int Criminol, 1 , 70–77.

Nivette, A. E., Zahnow, R., Aguilar, R., et al. (2021a). A global analysis of the impact of COVID-19 stay-at-home restrictions on crime. Nature Human Behaviour, 5 , 868–877.

Nivette, A. E., Zahnow, R., Aguilar, R., et al. (2021b). Supplementary materials for A global analysis of the impact of COVID-19 stay-at-home restrictions on crime. Nature Human Behaviour, 5 , 868–877.

Payne, J. L., Morgan, A., & Piquero, A. R. (2020). COVID-19 and social distancing measures in Queensland, Australia, are associated with short-term decreases in recorded violent crime. Journal of Experimental Criminology, 18 , 89–113.

Payne, J. L., Morgan, A., & Piquero, A. R. (2021). Exploring regional variability in the short-term impact of COVID-19 on property crime in Queensland, Australia. Crime Science, 10 (1), 1–20.

Perez-Vincent, S. M., Schargrodsky, E., & García Mejía, M. (2021). Crime under lockdown: The impact of COVID-19 on citizen security in the city of Buenos Aires. Criminology & Public Policy , 20 (3), 463–492.

Piquero, A. R., Jennings, W. G., Jemison, E., Kaukinen, C., & Knaul, F. M. (2021). Domestic violence during the COVID-19 pandemic—Evidence from a systematic review and meta-analysis. Journal of Criminal Justice, 74 , 101806.

Poblete-Cazenave, R. (2020). The Great Lockdown and criminal activity: Evidence from Bihar. India Covid Econ, 29 , 141–163.

Ratcliffe, J. H., & Taylor, R. B. (2023). The disproportionate impact of post-George Floyd violence increases on minority neighborhoods in Philadelphia. Journal of Criminal Justice, 88 , 102103.

Riddell, J. R., Piquero, A. R., Kaukinen, C., Bishopp, S. A., Piquero, N. L., Narvey, C. S., & Iesue, L. (2022). Re-opening Dallas: A short-term evaluation of COVID-19 regulations and crime. Crime & Delinquency , 68 (8), 1137–1160.

Rogers, M. L., & Pridemore, W. A. (2017). Geographic and temporal variation in cross-national homicide victimization rates. The Handbook of Homicide (pp. 20–43). London: Wiley.

Rossmo, D. K., & Summers, L. (2021). Offender decision-making and displacement. Justice Quarterly, 38 (3), 375–405.

Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66 (5), 688–701.

Stickle, B., & Felson, M. (2020). Crime rates in a pandemic: The largest criminological experiment in history. American Journal of Criminal Justice, 45 (4), 525–536.

Torrente, D., Gallo, P., & Oltra, C. (2017). Comparing crime reporting factors in EU countries. European Journal on Criminal Research, 23 (2), 153–174.

Vilalta, C., Fondevila, G., & Massa, R. (2023). The impact of anti-COVID-19 measures on Mexico City criminal reports. Deviant Behavior, 44 (5), 723–737.

Wallace, D., Walker, J., Nelson, J., Towers, S., Eason, J., & Grubesic, T. H. (2021). The 2020 coronavirus pandemic and its corresponding data boon: Issues with pandemic-related DATA from criminal justice organizations. Journal of Contemporary Criminal Justice, 37 (4), 543–568.

Wilcox, P., & Cullen, F. T. (2018). Situational opportunity theories of crime. Annual Review of Criminology, 1 , 123–148.

Xie, M., & Baumer, E. P. (2019). Crime victims’ decisions to call the police: Past research and new directions. Annual Review of Criminology, 2 (1), 217–240.

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Thanks to two anonymous reviewers and to the Reading Sessions in Quantitative Criminology (RESQUANT) group of the University of Manchester for their comments.

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Trajtenberg, N., Fossati, S., Diaz, C. et al. The heterogeneous effects of COVID-19 lockdowns on crime across the world. Crime Sci 13 , 22 (2024). https://doi.org/10.1186/s40163-024-00220-y

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Infectious Diseases Spike When Kids Return To School − Here’s What to Do

Sick kids can spread diseases in their households and classrooms..

Ready or not, back-to-school season is here, and kids will be bringing home more than homework. They’ll be carrying germs, too.

These viruses and bacteria will be agents that cause the common cold, influenza, norovirus, COVID-19, strep throat, and more. There’s a reason why boxes of tissues and sanitizing wipes show up on most school supply lists.

As a nursing professor with experience in public health promotion , I have spent the past several years helping the public understand how to prevent the spread of infectious diseases, especially the flu and COVID-19 .

Here are some ways to minimize illness transmission from school to home.

COVID-19 Cases Likely to Rise

In what healthcare providers call the summer wave , COVID-19 infections have been increasing throughout the summer of 2024. As of July 30, 2024, the number of COVID-19 infections has grown or is likely to grow in 35 states and territories .

Summer heat waves force people inside and into close proximity . And as record-setting temperatures continue driving this trend, the number of COVID-19 cases is expected to increase. This increase is expected to extend into the 2024–25 school year.

Currently, COVID-19 variants KP.3 and KP.3.1.1 are the dominant circulating virus strains . These variants are among the FLiRT variants , nicknamed after the locations of their spike protein mutations . These variants spread more easily from person to person and are able to evade vaccine-induced immunity better than previous strains .

These highly contagious COVID-19 strains, combined with the relaxed isolation guidelines that the Centers for Disease Control and Prevention released in March 2024, are likely to increase classroom disease transmission this coming school year. These updated CDC guidelines apply to all respiratory viruses, not just COVID-19.

The new guidelines recommend that everyone stay home when they are sick but also suggest that a person can return to normal activities once symptoms are improving and the person is fever-free for at least 24 hours without the use of fever-reducing medication. While it is too early to know the transmission impact of these guidelines, they may lead to more people coming out of isolation while still infectious.

The CDC’s March 2024 guidelines relaxed isolation recommendations.

Children play a large role in transmitting the COVID-19 virus. Children often do not have overt symptoms when they are infected with COVID-19, leading to increased contact and spread.

A two-year study following over 160,000 households composed of both adults and children found that just over 70% of viral transmissions , including COVID-19, in these households were pediatric index cases, meaning they started with a child. Further, these pediatric index cases were reduced by 60% to 80% during school breaks. This data suggests that COVID-19 infections will continue to increase once the school year starts.

Good Hygiene Habits Reduce the Spread

In May 2024, the CDC also released guidance for preventing classroom spread of infectious diseases , including COVID-19 and other common infectious diseases such as the flu, norovirus, and strep throat. This guidance places emphasis on proper respiratory etiquette, hand-washing, and vaccination.

Proper cough and sneeze hygiene is especially important to reduce transmission of diseases such as COVID-19 and the flu, which are commonly transmitted through respiratory droplets . Coughs and sneezes create respiratory droplets that can be full of viruses or bacteria. Because these droplets are forcefully expelled, they can be spread around the environment and inhaled by another person.

That’s why it is important to turn your face away from others , cover up coughing or sneezing with a tissue, and then quickly dispose of the tissue. If a tissue is not available, your sleeve is the next best option. Whichever method you use, it is important to wash your hands afterward. In addition to encouraging proper respiratory etiquette, classrooms should also have appropriate ventilation .

The CDC’s classroom guidance also emphasizes proper hand-washing . Up to 80% of infectious diseases are spread through touch, and classrooms have countless high-touch surfaces , including light switches, tabletops, shared supplies, doorknobs, sports equipment, and toys.

Proper hand-washing can prevent about 30% of diarrhea-related illnesses and about 20% of respiratory infections, such as colds and flu. The CDC also reports that proper hand-washing reduces absenteeism due to gastrointestinal illness by up to 57%.

Healthcare Providers Recommend COVID-19 and Flu Vaccines

Another important part of reducing classroom spread of infectious disease is keeping children up to date on vaccinations . Proper vaccination can reduce disease transmission rates by 40% to 50% for flu and COVID-19 , 80% for child pneumococcal cases , upward of 90% for chickenpox, and 100% for diseases such as polio and smallpox .

For the past several years, the CDC has recommended receiving the flu and COVID-19 vaccines at the same time when possible . Despite this recommendation, there has been some hesitancy in the uptake of both vaccines.

A 2024 Canadian study found that 20% of respondents did not see the benefit in co-administration, and another 17% were concerned about adverse reactions of receiving both vaccines together. However, several years of CDC data demonstrates the safety of receiving the flu and COVID-19 vaccines together .

Moderna recently released Phase 3 clinical trial data on a new combination vaccine against both the flu and COVID-19. This combination vaccine, currently called mRNA-1083, has demonstrated higher effectiveness when compared with individual vaccines for the flu or COVID-19. Moderna is expected to seek FDA approval soon. This combination vaccine may increase vaccine uptake because only one shot will be required instead of two.

Sick Kids Should Stay Home

The most important way to reduce the spread of germs in school is to follow the principle of keeping kids home when they’re sick . When sick kids go to school, they infect not only other students but teachers and staff too. When teachers get sick, it affects student learning and costs the U.S. billions of dollars each year.

Most schools and daycare centers have guidelines on when to keep a child at home . As a general rule, a child should stay home from school or daycare if they have a fever, vomiting, or diarrhea or if they are generally unwell and unable to participate fully in school.

Without the presence of a fever, it is OK to go to school with a cough or runny nose as long as the child feels well enough to participate in class. To return to school or daycare , the child should be fever-free for at least 24 hours without the use of fever-reducing medications. When a student is returning to school with respiratory symptoms, consider having them take extra precautions, such as using a mask to protect others for the next five days.

If you have concerns about whether to send your child to school, it’s always a good idea to seek advice from your healthcare provider.

Healthy Habits Boost the Immune System

Last but not least, focusing on healthy habits such as getting enough sleep and exercise , as well as eating nutritious meals , helps boost the immune system .

These actions should be practiced by family members of all ages.

Libby Richards is a Professor of Nursing at Purdue University. This article is republished from The Conversation under a Creative Commons license . Read the original article .

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Federal government to provide free COVID-19 tests starting in September

The U.S. Department of Health and Human Services announced Friday that American households would be eligible to order four COVID-19 tests starting in late September.

The free at-home tests provided by the federal government are capable of detecting new variants of the coronavirus, as officials report a nationwide surge in cases.

This announcement follows approval by the Food and Drug Administration of specialized COVID-19 vaccines designed to protect against new strains of the virus that have been prevalent in positive cases across the country this summer.

Despite this, Arizona cases have stayed "moderate," according to wastewater data from the U.S. Centers for Disease Control and Prevention.

But local health officials warned there could be a slight increase in positive cases as school starts again.

Here's what you need to stay safe and how to get free tests sent to your home.

Online requests for free tests return following previous success

According to the U.S. Department Health and Human Services, more than 900 million free COVID-19 tests were sent to American households since the pandemic began, regardless of insurance status.

Tests are ordered through the federal agency's online website and shipped directly to homes, according to the HHS.

Orders for new tests were currently closed but expected to open in late September on COVIDTests.gov , according to HHS. A specific date remained unclear.

The HHS did not immediately respond to The Arizona Republic's request for more information.

Volunteers sought: Does long COVID cause a cognitive disorder? A $2.5 million Arizona study aims to find out

How long are tests good for?

The new round of tests, available for order at COVIDTests.gov once the program relaunches in early fall, will be valid through the end of 2024.

At-home tests come with different expiration dates, some of which have been extended under an FDA emergency authorization. A complete list of these extensions can be found on the FDA's website.

Arizona's extreme heat could be particularly dangerous to COVID-19 tests, as the FDA warns extended exposure to high temperatures could impact the performance of the test.

The FDA recommended people who order free tests consider the shipping time frame to prevent tests from sitting outside any longer than during the usual shipping process.

Federal government says vaccines remain most effective way to protect against severe cases of COVID-19

On Thursday, the FDA approved emergency use of a new mRNA COVID-10 vaccine to combat variant strain Omicron KP.2.

The FDA found the well-tested vaccines were the foundation for COVID-19 prevention, according to Dr. Peter Marks, FDA director of biologics evaluation and research in a news release.

“These updated vaccines meet the agency’s rigorous, scientific standards for safety, effectiveness, and manufacturing quality," Marks wrote.

Vaccine makers are required to submit to three stages of clinical trials to ensure that formulas are safe and effective, according to the CDC. Clinical trials require real-world tests that involve tens of thousands of volunteers of different demographics, the CDC added.

What we know: Is Arizona experiencing a summer COVID-19 surge?

The updated vaccines include Comirnaty and Spikevax for individuals aged 12 and older, as well as Moderna and Pfizer-BioNTech, which have also produced vaccines authorized for individuals between 6 months and 11 years old.

"Given waning immunity of the population from previous exposure to the virus and from prior vaccination, we strongly encourage those who are eligible to consider receiving an updated COVID-19 vaccine to provide better protection against currently circulating variants," Marks wrote.

Watch CBS News

Government announces more COVID-19 tests can be ordered through mail for no cost

Updated on: August 24, 2024 / 2:40 PM EDT / CBS/AP

On the heels of a summer wave of  COVID-19 cases, Americans will be able to get free virus test kits mailed to their homes, starting in late September.

U.S. households will be able to order up to four COVID-19 nasal swab tests when the federal program reopens, according to the website, COVIDtests.gov. The U.S. Health and Human Services agency that oversees the testing has not announced an exact date for ordering to begin.

The tests will detect current virus strains and can be ordered ahead of the holiday season when family and friends gather for celebrations, an HHS spokesperson said in an emailed statement. Over-the-counter COVID-19 at-home tests typically cost around $11, as of last year.

The announcement also comes as the government is once again urging people to get an updated COVID-19 booster, ahead of the fall and winter respiratory virus season. 

Earlier this week the FDA announced it had greenlit updated  COVID-19  vaccines from Pfizer and Moderna for the 2024 fall season. Novavax is expected to get approval for its updated vaccine this year. The Centers for Disease Control and Prevention recommends  all Americans ages 6 months and older get a shot of the "updated 2024-2025 COVID-19 vaccine."

Vaccine uptake is waning, however. Most Americans have some immunity from prior infections or vaccinations, but data shows under a quarter of U.S. adults took last fall's COVID-19 shot.

The Biden administration has given out 1.8 billion COVID-19 tests, including half distributed to households by mail. It's unclear how many tests the feds have on hand.

Tens of billions of tax-payer dollars have been used to develop COVID-19 tests, vaccines and treatments.

Although deaths and serious infections have dropped dramatically since COVID-19 started its U.S. spread in 2020, hospitalizations have started to slightly creep up in recent weeks. In total, more than 1 million Americans have died from the virus.

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COVID guidelines have changed. Here’s when a sick kid can return to school

As schools reopen for another year, they are focused on improving student attendance. But  back-to-school  is hitting just as COVID-19 cases are increasing , raising the question: When is a child too sick for school?

School absences surged  during the pandemic and have yet to recover. Nearly 1 in 4 students remains chronically absent, defined as missing 10% or more of the academic year, according to the latest data analyzed by The Associated Press.

One reason for continued high absences: After years of COVID-19 quarantines, parents are more cautious about sending children to school when they might be contagious with an illness.

When a child misses school, even for an excused absence like a sick day, it’s harder for them to  stay on track academically . So schools and health experts are trying to change the culture around sick days.

Here’s what they want parents to know.

COVID guidelines have changed

During the pandemic, the Centers for Disease Control and Prevention urged people who tested positive for COVID-19 to isolate at home for a set number of days and to quarantine after exposure to the coronavirus. In some settings, people with any mild illness were urged to remain home until symptoms were clear.

Those standards, and the caution behind them,  remained for years  after schools reopened to in-person instruction. That meant children often missed large portions of school after contracting or being exposed to COVID-19 or other illnesses.

This spring, COVID-19 guidance officially changed. Now, the CDC suggests people  treat COVID-19 like other respiratory illnesses , such as the flu and RSV.

Fever-free for 24 hours

If a child has a fever, they should stay home, no matter the illness.

A child can return to school when their fever has been gone for 24 hours without fever-reducing medication. Other symptoms should be improving.

What about other symptoms?

If a child doesn’t have a fever, it’s OK to send them to class  with some signs of illness , including a runny nose, headache or cough, according to schools and the  American Academy of Pediatrics . If those symptoms aren’t improving or are severe, such as a hacking cough, call your child’s doctor.

The guidance around vomiting and diarrhea varies across school districts. Generally, students should remain home  until symptoms stop , according to American Academy of Pediatrics guidelines. Older children may be able to manage  mild diarrhea  at school.

“Unless your student has a fever or threw up in the last 24 hours, you are coming to school. That’s what we want,” said Abigail Arii, director of student support services in Oakland, California.

Guidance from the Los Angeles Unified School District says students can attend school with mild symptoms such as a runny nose or cold, but should stay home if they have vomiting, diarrhea, severe pain or a fever of 100 degrees Fahrenheit (37 degrees Celsius) or higher.

School districts across the U.S. have similar guidance, including in  Texas ,  Illinois  and  New York .

When to wear a mask

The CDC says people should  take additional precautions  for five days after returning to school or other normal activities.

Masks and social distancing are no longer mandated but are encouraged to prevent disease spread. Experts also recommend plenty of handwashing and taking steps for cleaner air, such as opening a window or running an air purifier.

School districts say parents should keep up-to-date on all health examinations and immunizations for students so they don’t miss additional days of school.

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Reporting by Richa Naidu; Editing by Matt Scuffham and Jan Harvey

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Thomson Reuters

Richa is a London-based reporter covering consumer goods companies, including their supply chains, advertising strategies, corporate governance structures, sustainability goals, and the political issues that impact them. She previously wrote about U.S. based retailers and consumer firms, major financial institutions and the Tokyo 2020 Olympic Games.

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