covid vaccination experience essay

8 Lessons We Can Learn From the COVID-19 Pandemic

BY KATHY KATELLA May 14, 2021

Note: Information in this article was accurate at the time of original publication. Because information about COVID-19 changes rapidly, we encourage you to visit the websites of the Centers for Disease Control & Prevention (CDC), World Health Organization (WHO), and your state and local government for the latest information.

The COVID-19 pandemic changed life as we know it—and it may have changed us individually as well, from our morning routines to our life goals and priorities. Many say the world has changed forever. But this coming year, if the vaccines drive down infections and variants are kept at bay, life could return to some form of normal. At that point, what will we glean from the past year? Are there silver linings or lessons learned?

“Humanity's memory is short, and what is not ever-present fades quickly,” says Manisha Juthani, MD , a Yale Medicine infectious diseases specialist. The bubonic plague, for example, ravaged Europe in the Middle Ages—resurfacing again and again—but once it was under control, people started to forget about it, she says. “So, I would say one major lesson from a public health or infectious disease perspective is that it’s important to remember and recognize our history. This is a period we must remember.”

We asked our Yale Medicine experts to weigh in on what they think are lessons worth remembering, including those that might help us survive a future virus or nurture a resilience that could help with life in general.

Lesson 1: Masks are useful tools

What happened: The Centers for Disease Control and Prevention (CDC) relaxed its masking guidance for those who have been fully vaccinated. But when the pandemic began, it necessitated a global effort to ensure that everyone practiced behaviors to keep themselves healthy and safe—and keep others healthy as well. This included the widespread wearing of masks indoors and outside.

What we’ve learned: Not everyone practiced preventive measures such as mask wearing, maintaining a 6-foot distance, and washing hands frequently. But, Dr. Juthani says, “I do think many people have learned a whole lot about respiratory pathogens and viruses, and how they spread from one person to another, and that sort of old-school common sense—you know, if you don’t feel well—whether it’s COVID-19 or not—you don’t go to the party. You stay home.”

Masks are a case in point. They are a key COVID-19 prevention strategy because they provide a barrier that can keep respiratory droplets from spreading. Mask-wearing became more common across East Asia after the 2003 SARS outbreak in that part of the world. “There are many East Asian cultures where the practice is still that if you have a cold or a runny nose, you put on a mask,” Dr. Juthani says.

She hopes attitudes in the U.S. will shift in that direction after COVID-19. “I have heard from a number of people who are amazed that we've had no flu this year—and they know masks are one of the reasons,” she says. “They’ve told me, ‘When the winter comes around, if I'm going out to the grocery store, I may just put on a mask.’”

Lesson 2: Telehealth might become the new normal

What happened: Doctors and patients who have used telehealth (technology that allows them to conduct medical care remotely), found it can work well for certain appointments, ranging from cardiology check-ups to therapy for a mental health condition. Many patients who needed a medical test have also discovered it may be possible to substitute a home version.

What we’ve learned: While there are still problems for which you need to see a doctor in person, the pandemic introduced a new urgency to what had been a gradual switchover to platforms like Zoom for remote patient visits. 

More doctors also encouraged patients to track their blood pressure at home , and to use at-home equipment for such purposes as diagnosing sleep apnea and even testing for colon cancer . Doctors also can fine-tune cochlear implants remotely .

“It happened very quickly,” says Sharon Stoll, DO, a neurologist. One group that has benefitted is patients who live far away, sometimes in other parts of the country—or even the world, she says. “I always like to see my patients at least twice a year. Now, we can see each other in person once a year, and if issues come up, we can schedule a telehealth visit in-between,” Dr. Stoll says. “This way I may hear about an issue before it becomes a problem, because my patients have easier access to me, and I have easier access to them.”

Meanwhile, insurers are becoming more likely to cover telehealth, Dr. Stoll adds. “That is a silver lining that will hopefully continue.”

Lesson 3: Vaccines are powerful tools

What happened: Given the recent positive results from vaccine trials, once again vaccines are proving to be powerful for preventing disease.

What we’ve learned: Vaccines really are worth getting, says Dr. Stoll, who had COVID-19 and experienced lingering symptoms, including chronic headaches . “I have lots of conversations—and sometimes arguments—with people about vaccines,” she says. Some don’t like the idea of side effects. “I had vaccine side effects and I’ve had COVID-19 side effects, and I say nothing compares to the actual illness. Unfortunately, I speak from experience.”

Dr. Juthani hopes the COVID-19 vaccine spotlight will motivate people to keep up with all of their vaccines, including childhood and adult vaccines for such diseases as measles , chicken pox, shingles , and other viruses. She says people have told her they got the flu vaccine this year after skipping it in previous years. (The CDC has reported distributing an exceptionally high number of doses this past season.)  

But, she cautions that a vaccine is not a magic bullet—and points out that scientists can’t always produce one that works. “As advanced as science is, there have been multiple failed efforts to develop a vaccine against the HIV virus,” she says. “This time, we were lucky that we were able build on the strengths that we've learned from many other vaccine development strategies to develop multiple vaccines for COVID-19 .” 

Lesson 4: Everyone is not treated equally, especially in a pandemic

What happened: COVID-19 magnified disparities that have long been an issue for a variety of people.

What we’ve learned: Racial and ethnic minority groups especially have had disproportionately higher rates of hospitalization for COVID-19 than non-Hispanic white people in every age group, and many other groups faced higher levels of risk or stress. These groups ranged from working mothers who also have primary responsibility for children, to people who have essential jobs, to those who live in rural areas where there is less access to health care.

“One thing that has been recognized is that when people were told to work from home, you needed to have a job that you could do in your house on a computer,” says Dr. Juthani. “Many people who were well off were able do that, but they still needed to have food, which requires grocery store workers and truck drivers. Nursing home residents still needed certified nursing assistants coming to work every day to care for them and to bathe them.”  

As far as racial inequities, Dr. Juthani cites President Biden’s appointment of Yale Medicine’s Marcella Nunez-Smith, MD, MHS , as inaugural chair of a federal COVID-19 Health Equity Task Force. “Hopefully the new focus is a first step,” Dr. Juthani says.

Lesson 5: We need to take mental health seriously

What happened: There was a rise in reported mental health problems that have been described as “a second pandemic,” highlighting mental health as an issue that needs to be addressed.

What we’ve learned: Arman Fesharaki-Zadeh, MD, PhD , a behavioral neurologist and neuropsychiatrist, believes the number of mental health disorders that were on the rise before the pandemic is surging as people grapple with such matters as juggling work and childcare, job loss, isolation, and losing a loved one to COVID-19.

The CDC reports that the percentage of adults who reported symptoms of anxiety of depression in the past 7 days increased from 36.4 to 41.5 % from August 2020 to February 2021. Other reports show that having COVID-19 may contribute, too, with its lingering or long COVID symptoms, which can include “foggy mind,” anxiety , depression, and post-traumatic stress disorder .

 “We’re seeing these problems in our clinical setting very, very often,” Dr. Fesharaki-Zadeh says. “By virtue of necessity, we can no longer ignore this. We're seeing these folks, and we have to take them seriously.”

Lesson 6: We have the capacity for resilience

What happened: While everyone’s situation is different­­ (and some people have experienced tremendous difficulties), many have seen that it’s possible to be resilient in a crisis.

What we’ve learned: People have practiced self-care in a multitude of ways during the pandemic as they were forced to adjust to new work schedules, change their gym routines, and cut back on socializing. Many started seeking out new strategies to counter the stress.

“I absolutely believe in the concept of resilience, because we have this effective reservoir inherent in all of us—be it the product of evolution, or our ancestors going through catastrophes, including wars, famines, and plagues,” Dr. Fesharaki-Zadeh says. “I think inherently, we have the means to deal with crisis. The fact that you and I are speaking right now is the result of our ancestors surviving hardship. I think resilience is part of our psyche. It's part of our DNA, essentially.”

Dr. Fesharaki-Zadeh believes that even small changes are highly effective tools for creating resilience. The changes he suggests may sound like the same old advice: exercise more, eat healthy food, cut back on alcohol, start a meditation practice, keep up with friends and family. “But this is evidence-based advice—there has been research behind every one of these measures,” he says.

But we have to also be practical, he notes. “If you feel overwhelmed by doing too many things, you can set a modest goal with one new habit—it could be getting organized around your sleep. Once you’ve succeeded, move on to another one. Then you’re building momentum.”

Lesson 7: Community is essential—and technology is too

What happened: People who were part of a community during the pandemic realized the importance of human connection, and those who didn’t have that kind of support realized they need it.

What we’ve learned: Many of us have become aware of how much we need other people—many have managed to maintain their social connections, even if they had to use technology to keep in touch, Dr. Juthani says. “There's no doubt that it's not enough, but even that type of community has helped people.”

Even people who aren’t necessarily friends or family are important. Dr. Juthani recalled how she encouraged her mail carrier to sign up for the vaccine, soon learning that the woman’s mother and husband hadn’t gotten it either. “They are all vaccinated now,” Dr. Juthani says. “So, even by word of mouth, community is a way to make things happen.”

It’s important to note that some people are naturally introverted and may have enjoyed having more solitude when they were forced to stay at home—and they should feel comfortable with that, Dr. Fesharaki-Zadeh says. “I think one has to keep temperamental tendencies like this in mind.”

But loneliness has been found to suppress the immune system and be a precursor to some diseases, he adds. “Even for introverted folks, the smallest circle is preferable to no circle at all,” he says.

Lesson 8: Sometimes you need a dose of humility

What happened: Scientists and nonscientists alike learned that a virus can be more powerful than they are. This was evident in the way knowledge about the virus changed over time in the past year as scientific investigation of it evolved.

What we’ve learned: “As infectious disease doctors, we were resident experts at the beginning of the pandemic because we understand pathogens in general, and based on what we’ve seen in the past, we might say there are certain things that are likely to be true,” Dr. Juthani says. “But we’ve seen that we have to take these pathogens seriously. We know that COVID-19 is not the flu. All these strokes and clots, and the loss of smell and taste that have gone on for months are things that we could have never known or predicted. So, you have to have respect for the unknown and respect science, but also try to give scientists the benefit of the doubt,” she says.

“We have been doing the best we can with the knowledge we have, in the time that we have it,” Dr. Juthani says. “I think most of us have had to have the humility to sometimes say, ‘I don't know. We're learning as we go.’"

Information provided in Yale Medicine articles is for general informational purposes only. No content in the articles should ever be used as a substitute for medical advice from your doctor or other qualified clinician. Always seek the individual advice of your health care provider with any questions you have regarding a medical condition.

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It’s that time of year again – vaccine season. While most people can appreciate that vaccination is an amazing achievement, their enthusiasm might falter when it comes time to schedule and receive their own. And new research suggests that might influence how the vaccine affects them. 

Recently, researchers at Stanford University, Miami University, and the University of California, San Francisco, studied the effects of different types of positive and negative mindsets regarding the COVID-19 vaccine. Their work, published in the journal Brain, Behavior & Immunity – Health , suggests that a positive mindset is associated with more positive outcomes, such as less stress and side effects, better mood, and possibly even better immune response. 

Details of the findings include:

All positive vaccine-related mindsets predict lower anxiety on the day of the appointment, and lower stress and sadness – and more happiness – in the days around vaccination. 

A positive mindset about the efficacy of the vaccine and how the body will respond to vaccination were linked to fewer negative physical side effects.

The vaccine mindset that side effects indicate “ the vaccine is working!”  was associated with better immune response – specifically, higher antibodies six months later.

“Many people will be surprised by these findings, but they shouldn’t be,” said the authors. “Our brains are connected to every physiological system in our bodies, and we know from decades of previous research on placebo effects and psychoneuroimmunology that our mindsets can influence physiological outcomes, including the immune system.” 

Below, study authors Darwin Guevarra of Miami University and UCSF, Alia Crum of Stanford, and Elissa Epel, BA ’90, of UCSF describe some of the most important takeaways from their study and share how people can apply this science to try to improve their own vaccines experiences.  

1. What is the #1 lesson you’d want people to take away from this study? 

Mindsets are beliefs and assumptions about how the world works that can impact what people experience, feel, and do. The main lesson from the study is that your mindsets about vaccines can impact your post-vaccination experience in terms of how you feel, the side effects you experience, and, in some cases, your immune response.

In this study, we were specifically interested in a number of different mindsets, including the mindset that the vaccine will work, the mindset that your body will be responsive to the vaccine, and the mindset that side effects are signs that the vaccine is working. All the mindsets were associated with more positive experiences with the vaccine to some degree (e.g., less anxiety or fewer side effects). However, the mindset about side effects was most strongly associated with a stronger neutralizing antibody response, a physiological marker of vaccine efficacy. 

This being said, it might be easy to misinterpret the findings as “mindsets about the vaccine directly cause better vaccination outcomes.” However, this study only shows a correlation between mindset and outcomes, meaning we cannot say the link is causal. Additional experiments are needed to claim causality.

2. Why are the results related to side effects important?

The findings regarding side effects mindset are particularly important because fear of side effects is the most common reason for vaccine hesitancy. While we cannot deny the reality of vaccine side effects, we can accurately inform people that many side effects are signs that the vaccine is working to boost your immune response. Common side effects of the vaccines, like muscle soreness, headache, and fever, are encouraging indications that the vaccine is working as it should and the body is building immunity to COVID-19. In fact, in a separate paper  based on this same group of participants, the results showed that greater sickness symptoms – assessed through self-reporting and a bio-sensor – predicted stronger long-term antibody response.

Yet many people don’t seem to recognize this – and this is a missed opportunity. Helping people to rethink side effects as positive signs can transform them from an unpleasant sensation into a favorable signal. This can improve the vaccine experience and may even lead to a better immune response.  

3. If someone already has a negative or anxious mindset about the vaccine, what can they do to develop a more positive mindset?

People can move to more positive mindsets about the vaccine simply by being more informed about the true effects and mechanisms of the vaccine. In doing this, they should lean on accurate information that educates them about how vaccines work and, in particular, work to understand that side effects are often a sign that the vaccine is doing its job. Side effects are not entirely random. 

Promoting a positive mindset before your vaccination

Positive side effect mindset: Side effects are a good sign. They are part of my immune response. They mean the vaccine is working. My body is working hard to produce antibodies.

Positive body response mindset: My body is capable and will respond well to the vaccine. I trust my body’s natural ability to strengthen its defenses through this vaccination.

Vaccine effectiveness mindset: The vaccine will work and protect me from the virus. The vaccine helps my immune system recognize and fight the virus more effectively if exposed to the COVID-19 virus.

The COVID-19 vaccines give your body a practice run against the virus, teaching it what COVID-19 looks like and helping your body build COVID-19-neutralizing antibodies. This results in immunity because now your body has, within it, what it needs to fight the virus should it encounter it again. Sometimes this process causes side effects. And side effects like muscle stiffness, soreness, aches, headaches, nausea, and generally feeling under the weather, are part of the body’s biological processes promoting vaccine’s efficacy .

4. Is there anything else you want people to understand about this topic?

It’s important to remember that our body’s responses to anything – the medications we take, the foods we eat, and the stress we experience – are influenced by our mindsets as well as the objective properties of those things. And this is also true of the COVID-19 vaccine. Our mindsets about the vaccine can affect not just how we feel afterward but also our experience with side effects. And in some instances, your mindset about the vaccine's side effects can potentially influence your immune response.

For more information

Ethan Dutcher and Aric Prather of UCSF are also co-authors of this study. Crum is an associate professor of psychology in the  School of Humanities and Sciences at Stanford. She is also a member of  Stanford Bio-X , the  Wu Tsai Human Performance Alliance , the  Maternal & Child Health Research Institute (MCHRI) , and the Stanford Cancer Institute .

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• Published: 28 January 2022

Impact of vaccination on the COVID-19 pandemic in U.S. states

• Xiao Chen 1 ,
• Hanwei Huang 2 , 3 ,
• Jiandong Ju 4 ,
• Ruoyan Sun 5 &
• Jialiang Zhang 4  

Scientific Reports volume  12 , Article number:  1554 ( 2022 ) Cite this article

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Governments worldwide are implementing mass vaccination programs in an effort to end the novel coronavirus (COVID-19) pandemic. Here, we evaluated the effectiveness of the COVID-19 vaccination program in its early stage and predicted the path to herd immunity in the U.S. By early March 2021, we estimated that vaccination reduced the total number of new cases by 4.4 million (from 33.0 to 28.6 million), prevented approximately 0.12 million hospitalizations (from 0.89 to 0.78 million), and decreased the population infection rate by 1.34 percentage points (from 10.10 to 8.76%). We built a Susceptible-Infected-Recovered (SIR) model with vaccination to predict herd immunity, following the trends from the early-stage vaccination program. Herd immunity could be achieved earlier with a faster vaccination pace, lower vaccine hesitancy, and higher vaccine effectiveness. The Delta variant has substantially postponed the predicted herd immunity date, through a combination of reduced vaccine effectiveness, lowered recovery rate, and increased infection and death rates. These findings improve our understanding of the COVID-19 vaccination and can inform future public health policies.

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Introduction.

The novel coronavirus (COVID-19) pandemic has had a devastating impact on health and well-being, with more than 131 million cases and 2.8 million deaths across more than 200 countries 1 as of early April 2021. Despite various regional and national non-pharmaceutical interventions 2 , 3 , 4 such as travel restrictions, social distancing measures, stay-at-home orders, and lockdowns, many countries continue to struggle with the growth of COVID-19 cases. It is obvious that a successful COVID-19 vaccination program is needed to end the pandemic and allow a return to normal life 5 , 6 .

By the end of February 2021, two COVID-19 vaccines had been approved in the U.S.: BNT162b2 (Pfizer/BioNTech) and mRNA-1273 (Moderna) 7 . In two large randomized controlled trials (RCTs), the Pfizer vaccine exhibited an efficacy of 95% (95% confidence interval [CI], 90.3%–97.6%) 8 in preventing COVID-19, and the Moderna vaccine showed an efficacy of 94.1% (95% CI, 89.3%–96.8%) 9 . Both are mRNA vaccines that require two doses to complete vaccination and received emergency use authorization by the U.S. Food and Drug Administration in December 2020 10 . Mass vaccination campaigns with these two vaccines have since begun. By early March 2021, more than 121 million doses had been administered across the U.S., with over 43 million individuals (~ 13% of the population) fully vaccinated with two doses 11 .

Although the efficacies of these two vaccines were shown to be high in RCTs, there is limited information on their potential population-level impact on the COVID-19 pandemic. One peer-reviewed study that estimated vaccine effectiveness used data from nationwide mass vaccination in Israel and reported the effectiveness of the Pfizer vaccine to be 46% (95% CI, 40%–51%) after the first dose and 92% (95% CI, 88%–95%) after the second dose for documented infection 12 . Another study that examined the effectiveness of the Pfizer vaccine among U.S. residents in skilled nursing facilities reported an estimation of 63% (95% CI, 33%–79%) after the first dose 13 .

In this study, we employed well-established reduced-form econometric techniques 14 , commonly used to evaluate the effects of policies or events 15 , 16 , to assess the impact of early-stage vaccination during the ongoing outbreak using data from all 50 U.S. states and the District of Columbia (DC). Although the allocation of vaccines is roughly proportional to state population (Extended Data Fig.  1 a), the actual proportion of the vaccinated population differs significantly across states over time (Extended Data Fig.  1 b), which provides the key variation to identify the impact of vaccination. Effectively, the observations from each region in the weeks before the vaccination program served as the “control” for the observations after the vaccination program began (“treatment”), with variations in the vaccination rates leading to changes in the “treatment intensity.” By comparing the outcomes across states before and after the initiation of vaccination programs, we evaluated the impact of vaccination on the COVID-19 pandemic.

COVID-19 events and vaccination timeline in the U.S. from 12 October 2020 to 7 March 2021. The red curve is the fraction of population infected over time (left y-axis). The solid blue curve is the cumulative vaccination coverage in the population with at least one dose of vaccine (right y-axis). The dashed blue curve is the cumulative vaccination coverage of fully vaccinated individuals in the population (right y-axis).

Study design

We collected state-level daily infection and hospitalization data in the U.S. from 12 October 2020 to 7 March 2021. Figure  1 shows a timeline of COVID-19 developments during this period, including important events and vaccination timeline. We aggregated the data to a weekly level in our baseline estimation given the observed weekly cycle 17 , 18 (see Extended Data Table 3 for results using daily data). The dependent variables used to assess the impact of vaccination on the pandemic are the growth rates of total cases and hospitalizations. Our key independent variables are vaccination rates, including the total number of vaccine doses administered per 100 people (at least one dose) and the total number of second doses administered per 100 people. Without any control variables, Fig.  2 shows the negative correlation between the vaccination rate and the growth rates of total cases and hospitalizations.

COVID-19 infections (total cases and hospitalizations) and vaccination rate. Vaccination rate is the number of individuals vaccinated per hundred. The solid line in each figure is a fitted linear curve between the growth rate of total cases/hospitalizations and vaccination rate. ( a ), Association between the growth rate of total cases and at least 1 dose of vaccination (coefficient = − 0.006, R 2  = 35.3%). ( b ) Association between the growth rate of total cases and 2 doses of vaccination (coefficient = − 0.013, R 2  = 28.6%). ( c ) Association between the growth rate of total hospitalizations and at least 1 dose of vaccination (coefficient = − 0.003, R 2  = 20.8%). ( d ), Association between the growth rate of total hospitalizations and 2 doses of vaccination (coefficient = − 0.007, R 2  = 16.6%).

We analyzed data in the U.S. from 12 October 2020 to 7 March 2021 for three main reasons. First, we selected similar number of weeks for the pre-treatment and post-treatment periods to balance the sample in our baseline results. Second, two important variables, growth of total hospitalizations and testing, are only available till early March 2021. Third, the Delta variant started to spread since March 2021 and became the dominant strain in the U.S. by July 2021. The presence of the Delta variant has significantly changed vaccine effectiveness, along with infection rate and recovery rate 19 . Thus, we chose to examine the effect of early-stage vaccination (till 7 March 2021) in our main text, and leave the analysis of extended data up to 17 November 2021 in the Discussion.

To make the individual states as comparable as possible, we first accounted for observable factors associated with the COVID-19 pandemic based on previous studies (see Extended Data Table 1 ). These time-varying control variables included non-pharmaceutical interventions 5 , 6 , 7 , election rallies 20 , 21 and anti-racism protests 22 that involved mass gatherings, and climate measures of snow depth and temperature 23 . To address the concern that changes in the number of total cases reflect the testing capacity of each state 24 , we also controlled for each state’s testing capacity. As the proportion of susceptible individuals declines, the infection rate may slow; therefore, we included the share of susceptible individuals in the regressions. We estimated the dependent variables of COVID-19 cases and hospitalizations with a one-week lag to account for the latency period of infection. Finally, we added state fixed effects and time fixed effects to capture spatial and temporal invariants to alleviate omitted-variable bias.

Impact of vaccination

Our data show that the national average weekly growth rate of total cases was 7% (s.e.m. = 5%) between 12 October 2020 and 7 March 2021. At the individual state level, the average growth rate was highest in Vermont (11%) and lowest in Hawaii (4%). The average growth rate of total hospitalizations across the 35 states that reported hospitalization data was 5% (s.e.m. = 4%); the highest growth rate was seen in Montana (8%) and the lowest in New Hampshire (2%).

Vaccination has significantly slowed the growth of total COVID-19 cases and hospitalizations in the U.S. Our baseline results (Fig.  3 a and Extended Data Table 2 ) show that one additional vaccinated individual per 100 people (at least 1 dose) reduced the growth rate of total cases by 0.7% (s.e.m. = 0.2%) and the growth rate of total hospitalizations by 0.7% (s.e.m. = 0.2%). The effects of receiving full vaccination with two doses appear greater, with reductions of 1.1% (s.e.m. = 0.4%) in the growth rate of total cases and 1.1% (s.e.m. = 0.3%) in total hospitalizations. Based on these estimates, vaccination reduced the number of new cases during our study period by 4.4 million (from 33.0 to 28.6 million), which translates into a decrease of 1.34 percentage points in the population infection rate (from 10.10% to 8.76%). Vaccination further reduced the number of hospitalizations by approximately 0.12 million, from 0.89 to 0.78 million (Fig.  3 b and Supplementary Methods).

Estimated effects of vaccination on the COVID-19 pandemic. Blue markers are the estimated effects of at least 1 dose of vaccine, and red markers are the estimated effects of 2 doses of vaccine. ( a ) Baseline effect of vaccination on the growth rates of total cases and hospitalizations. ( b ) Estimated trajectories of total cases and hospitalizations without vaccines (dashed curves) versus actual trajectories of total cases and hospitalizations with vaccines (solid curves).

If systematic correlations existed between the pre-vaccination growth rates of infection and hospitalization and the rate of vaccination, our results would have been subject to selection bias. However, this was not the case. We demonstrated that the number of vaccines allocated to each state was proportional to its population size (Extended Data Fig.  1 a). More importantly, we found that the pre-vaccination average growth rates of total cases and hospitalizations were not correlated with the average vaccination rate (Extended Data Fig.  2 ).

Our baseline results focus on the average treatment effect of vaccination. This effect may be heterogeneous across states that have different characteristics. For example, some evidence shows that the prevalence of COVID-19 differs across age groups, with older adults bearing the highest risk 25 , 26 . Because older adults were given priority during the rollout of vaccination, it is intuitive to ask whether this strategy made a difference. We separated the states into two groups according to their proportion of older adults (at least 65 years of age). Despite the slightly larger point estimate for the states with a share of older adults above the national median, the results do not differ significantly from those for the states below the median (Extended Data Fig.  3 c). In addition to age, we conducted heterogeneity tests on political affiliation, nonpharmaceutical interventions, race, income, and vaccine brand. We found no significant heterogeneous effect of vaccination on any of these characteristics (Extended Data Fig.  3 ), implying that COVID-19 vaccines have similar effectiveness across these characteristics.

We conducted a range of sensitivity tests. First, instead of using weekly data, we ran regressions with daily data and obtained results of similar magnitudes (Extended Data Table 3 ). Second, we used alternative measures to capture the development of the pandemic, including the logarithms of new cases and hospitalizations and the changes in logarithms of total cases and hospitalizations. Again, using these measures, we found that vaccination has significantly slowed the pandemic (Extended Data Fig.  4 and Extended Data Table 4 ). Although the vaccination rollout began on 14 December 2020, our vaccination data begin 11 January 2021; we thus used linear extrapolation to impute the missing data. Our results with the inclusion of imputed data are very similar to the baseline results (Extended Data Fig.  5 ). Finally, we selected approximately the same number of weeks for the pre-treatment and post-treatment periods to balance the sample in our baseline results. To check the sensitivity of our results to the sample period, we ran our regressions with varying time windows, and our results remain remarkably stable. We obtained approximately the same coefficients for sample periods from 18 to 45 weeks (Extended Data Fig.  6 ).

Estimated herd immunity date, cumulative vaccination coverage, and cumulative infection rate with different vaccination pace. Herd immunity date is predicted using first-dose vaccine effectiveness and first-dose vaccination pace (see " Methods "). Vaccination pace is the number of vaccine doses administered per 100 people per week. Until the first week of March 2021, the average pace over time is 2.08 doses per 100 people per week. The red curve is the predicted herd immunity date (left y-axis) in both ( a ) and ( b ). The blue curve is the estimated cumulative vaccination coverage in the population (right y-axis) when herd immunity is achieved in a and the estimated cumulative infection rate (right y-axis) in ( b ).

Herd immunity

To predict how the pandemic will develop with vaccines, and especially when herd immunity might be achieved, we built a Susceptible–Infected–Recovered (SIR) model with vaccination and calibrated it to our data. We also aim to identify important factors that substantially affect the predicted date of herdy immunity. Our model predictions of the infection rate during the study period showed 99.69% correlation with the empirical data at the national level by early March 2021 (Extended Data Fig.  7 ). Herd immunity is achieved in the model when the real-time basic reproduction number is less than one (Supplementary Methods).

According to our model predictions, at the national average vaccination pace of 2.08 doses per 100 people per week between January and early March of 2021, the U.S. would achieve herd immunity around the last week of July 2021, with a cumulative vaccination coverage rate of 60.2% and a cumulative infection rate of 13.3%. To understand how the speed of vaccination rollout would affect the time needed to reach herd immunity, we simulated herd immunity dates by varying vaccination pace (Fig.  4 ). We observed a general trend that a faster vaccination pace would allow the U.S. to achieve herd immunity sooner, but with a greater number of total vaccine doses administered and a lower cumulative infection rate. This result can be explained as more individuals gaining immunity from vaccines than from infections if the vaccination pace increases.

Our predictions of herd immunity assume a continuation of vaccine uptake. In reality, however, a few potential factors could affect uptake. A certain proportion of the population might not receive the vaccination due to vaccine hesitancy. Studies have shown that vaccine hesitancy is a common phenomenon in the U.S. 27 , 28 , where some individuals are reluctant to receive vaccines due to the perceived risks versus benefits, certain religious beliefs, and a lack of trust in government 28 . Another issue is the effectiveness of vaccines against new coronavirus variants 29 . For example, vaccine effectiveness is lower against the Delta variant and it remains unclear how the vaccine is effective at preventing the Omicron variant 30 , 31 .

To examine how vaccine hesitancy and changes in vaccine effectiveness could affect our predictions for herd immunity, we incorporated in our model a range of potential vaccine hesitancy and vaccine effectiveness estimates. We assumed that if x% of the population is hesitant, then cumulative vaccination coverage in each state will stop when (1 − x%) of the population is vaccinated. Table 1 shows that a higher percentage of vaccine-hesitant individuals will lead to lower vaccination coverage with more individuals infected with COVID-19 at herd immunity. We also tested a range of vaccine effectiveness values and presented the results in Table 1 . In general, higher vaccine hesitancy and lower vaccine effectiveness postpone the model-predicted herd immunity date. We further discuss the potential impact of the Delta variant on herd immunity date in the Discussion with updated data.

To examine whether our main study results hold in later stages of the vaccination program, we extended our empirical analysis to 14 November 2021. Due to data limitations, we can only update one outcome measure, the growth of COVID-19 cases, but not hospitalization. As shown by Extended Data Fig.  8 , vaccination (both at least one dose and two doses) is always negatively associated with the growth of total cases, but the magnitude of the estimated effect declines and the statistical significance gradually disappears with extended study time. This finding is not unexpected. First, there is evidence that the protection offered by vaccines against COVID decreases over time 19 , 32 . Second, the Delta variant became the dominant strain in the U.S. by mid-summer 2021. Studies have shown that the vaccines have lower effectiveness against the Delta variant 30 , 31 .

We also incorporated updated data on vaccine hesitancy and vaccine effectiveness in our SIR model to predict herd immunity. The weighted first-dose vaccine effectiveness is reduced to 52.28% against the Delta variant 30 , 31 . Based on vaccination coverage data in November, around 70% of the U.S. population received at least one dose. We thus approximated vaccine hesitancy in the population to be 30%. Additionally, researchers estimated the infectiousness of the Delta variant to be 40–60% higher than previous variants 33 , along with longer median duration (18 vs 13 days) and lower recovery rate (calculated as 1/duration) 34 . With these updated parameter estimates, our SIR model predicts the new herd immunity date to be around May 2022 (140% infection rate and 70% removal rate). We tested a range of possible values of the infection rate and removal rate (recovery rate + death rate) and show our results in Extended Data Table 4 . In general, a lower removal rate tends to delay the herd immunity date, but the effect of a higher infection rate is ambiguous as agents can also get immunity via faster infection.

Our model has a few limitations. First, due to the lack of valid COVID recovery data from a few states, we imputed the missing removal rate in those states to be the national median. To address the problem of missing values, we conducted robustness checks on the removal rate. The results indicated that the national median value, which is our baseline, fits the data better, and the herd immunity date is not very sensitive to variations in removal rate (Extended Data Fig.  9 ). Second, our model does not consider some recent developments of the pandemic, given that it is designed to model the early stage of the vaccination program. It does not take into account new variants such as the Delta or the Omicron. Our SIR model also assumes that only susceptible individuals undergo vaccination. However, in real life, many individuals who recovered from COVID later received vaccines. The biggest limitation is the inherent assumption of an SIR model, permanent immunity, which is not true in the long run due to decreased COVID vaccine protection and the appearance of new variants. Modifying our model to an SIRS model may better capture the temporary immunity brought by COVID vaccines. That being said, our model provides valid predictions based on early-stage vaccination trends.

Some additional limitations include the discontinuation of non-pharmaceutical interventions and changes in individual attitudes/behaviors towards the pandemic. Our model assumes a continuation of the non-pharmaceutical interventions in place in early March. Relaxation of these policies would likely increase the time needed to reach herd immunity. Another issue is moral hazard, that is, whether vaccinated individuals will change their behaviors and undertake more social interaction 35 . This change could result in higher risks of infection and a delay in reaching herd immunity.

Our study provides strong evidence that vaccination has significantly decreased COVID-19 cases and hospitalizations in the U.S. Following the vaccination trends between January and early March in 2021, our model predicts that herd immunity can be achieved earlier with faster vaccination pace and lower vaccination hesitancy. However, a few factors, such as moral hazard and variants of the SARS-CoV-2 virus, could lead to changes and cast doubt as to whether herd immunity can be achieved after all.

Data collection and processing

A summary is provided of the data used in our analysis. Our supplementary notes give further details, including a summary statistics table for all variables.

Epidemiological and vaccination data

We collected our state-level epidemiological data (total COVID-19 cases, hospitalization, and tests) from the COVID Tracking Project 36 , a commonly cited source 37 , 38 , 39 . The vaccination data across states were obtained from the U.S. Centers for Disease Control and Prevention’s (CDC) COVID data tracker 40 , where “people vaccinated” reflects the total number of people who have received at least one vaccine dose, and “people fully vaccinated” reflects the number who have received both doses prescribed by the vaccination protocol. We downloaded the CDC vaccination data from an open-source GitHub project by Our World in Data 41 . Both the BNT162b2 (Pfizer/BioNTech) vaccine and the mRNA-1273 (Moderna) vaccine require two doses 9 . In addition, the CDC shares data on COVID-19 vaccine distribution allocations by state for both the Pfizer 42 and Moderna 43 vaccines, as provided by the Office of the Assistant Secretary for Public Affairs under the U.S. Department of Health & Human Services.

Nonpharmaceutical interventions

In addition to epidemiological data, we obtained information on nonpharmaceutical intervention policies. We adopted the policy stringency index constructed by the Oxford COVID-19 Government Response Tracker 44 , which systematically collects information on various policy responses implemented by various governments in response to the pandemic. We focused on the policy category of “containment and closure,” which covers eight policies: school closings, workplace closings, cancelation of public events, restrictions on gathering sizes, cessation of public transportation, stay-at-home requirements, restrictions on internal movement, and restrictions on international travel. This stringency index is a weighted score across these eight containment and closure policies and is scaled between 0 and 100. A detailed explanation of these measures was given by Hale et al. (2021) 45 . We determined the stringency index for each state on a weekly basis by averaging the daily data.

Meteorological data

Another set of important independent variables included in this study regarded the local climate. We obtained station-level hourly weather data provided by the National Centers for Environmental Information 46 . These station-level weather data were then matched with the station location and corresponding state provided by the Global Historical Climatology Network Daily 47 . We calculated the average values from these weather reports for each week across all stations within each state. Given the lack of humidity data, temperature and snow depth were used as our climate measures.

Election rallies and black lives matter (BLM) demonstrations

Several large-scale mass gatherings for political campaigns and protests also occurred during our study period. We constructed binary measures for election rallies 48 . For states with a rally during week t , this binary measure takes the value of 1 for week t and for the week after ( t  + 1). Our BLM data from Elephrame offered detailed information (date, location, etc.) about each demonstration from news reports 49 , which were extracted using a Web scraper. We then calculated the total number of demonstrations that occurred across all cities within each state for each week.

Sociodemographic data

We also collected the sociodemographic characteristics of each state’s population using 2019 estimates from the U.S. Census Bureau 50 , 51 . Specifically, we downloaded data on age, race, and income. We constructed each of our sociodemographic variables to be binary, above or below the national median. We derived the proportion of individuals 65 years of age and older in the population, the proportion of the white population, and the income for each state to calculate a national median. Finally, our data for the 2020 Electoral College results were obtained from the National Archives 52 . We classified the states into those won by Joe Biden and those by won by Donald Trump.

Econometric analysis

Reduced-form analysis.

The following reduced-form empirical model was used to estimate the impact of vaccination on the pandemic:

Here, \({y}_{it}\) is the dependent variable that measures the growth of either total cases or total hospitalizations in state i at period t . Our baseline measure is the growth rate, which is defined as \(\frac{{{C}_{i,t}-C}_{i,t-1}}{{C}_{i,t-1}}\) for total cases and \(\frac{{{H}_{i,t}-H}_{i,t-1}}{{H}_{i,t-1}}\) for total hospitalizations, where \({C}_{i,t}\) and \({H}_{i,t}\) are the cumulative numbers of cases and hospitalizations. Alternative outcome measures were also used in the sensitivity analysis (Extended Data Fig.  4 ).

Our key independent variable, \({Vaccination}_{i,t-1}\) , is the rate of vaccination of state i in period t- 1, and \({a}_{1}\) is the coefficient of interest. We used two measures of vaccination rate: the number of vaccinated people (i.e., those who had received at least one dose of vaccine) per hundred and the number of fully vaccinated people (i.e., those who had received two doses of vaccine) per hundred. As the proportion of susceptible individuals in the total population decreases over time, the growth rate of infection may also decline. To deal with this intrinsic dynamic, \({S}_{i,t-1}/{L}_{i}\) was included in the regression model to control for the stock of susceptible individuals \({S}_{i,t-1}\) in the total population \({L}_{i}.\) We measured \({S}_{i,t-1}\) as the difference between the population size and the total number of infections. To adjust for differences in testing intensity across states, we added \({Test}_{i,t-1}/{L}_{i}\) to control for the number of tests relative to the total population.

Our control variables, \({X}_{i,t}\) , contain a dummy variable \({rally}_{i,t}\) , which equals 1 when an election rally occurred in state i at period t . We also added a variable \({protest}_{i,t}\) , which is the number of protests held across all cities in state i at period t . To capture the influence of climate on the pandemic, we included measures of state-level meteorological conditions, including average temperature, temperature deviation from the state mean, and the logarithm of the average snow depth. Note that we included state fixed effects ( \({b}_{i}\) ) to capture state-specific unobserved factors, which are time-invariant, such as location, geography, and culture. We also included week fixed effects \(\left({c}_{t}\right)\) to capture unobserved shocks, which are common across states, such as macroeconomic conditions. Finally, \({\varepsilon }_{i,t}\) is a random error term of the model, which has a mean of zero.

We estimated Eq. ( 1 ) using the method of Ordinary Least Square with weekly data for 50 states and DC in the baseline. Robust standard errors for the estimated coefficients with two-way clustering were calculated at the state and week levels 53 . Therefore, we allowed for within-state autocorrelation in the error term to capture the persistence of the pandemic within each state. We also allowed for spatial autocorrelation in the error term to capture common pandemic shocks or systematic misreporting across states.

Model summary

We modified a conventional SIR model with the addition of vaccination to simulate the development of the COVID-19 pandemic in the U.S. with vaccine rollout, including both state-level and national-level estimates. The theoretical SIR model with vaccination is as follows:

Here, \({S}_{i,t}\) is the state-specific ( i ) and time-varying ( t ) proportion of susceptible individuals in the population, \({I}_{i,t}\) is the proportion of infected individuals, and \({R}_{i,t}\) the proportion of recovered (plus dead) individuals. \({\beta }_{i,t}\) is the infection rate, which determines the spread of the pandemic. \({\gamma }_{i}\) includes both recovered individuals and deaths and is referred to as the removal rate 5 . Here \({\gamma }_{i}\) varies only by state and not over time. \({\delta }_{i,t}\) is the proportion of vaccinated individuals, and \({e}_{t}\) is the population-level vaccine effectiveness, which remains the same across states but may change in simulations to capture the effect of new variants.

We fit the SIR model above with state-level COVID-19 epidemiology data, from which we observed data on the cumulative number of cases, deaths, and vaccination doses administered. Only 29 of the 51 states (counting DC as a “state” for this purpose) reported valid recovery data. We imputed the missing data for the other 22 states with the median recovery and mortality rates from the known 29 states (see Supplementary Methods for details). We first estimated the infection rate ( \({\beta }_{i,t}\) ) and vaccination coverage ( \({\delta }_{i,t}\) ). To capture the impact of nonpharmaceutical interventions on the spread of COVID-19 4 , 5 , 6 , we used the following equation to estimate the infection rate with state fixed effect ( \({\rho }_{i}\) ) and time fixed effect ( \(\rho_{t}\) ):

Similarly, we estimated vaccination coverage using the following equations, controlling for state and time fixed effects.

We adopted two vaccination measures in our data: the total number of people who had received at least one vaccine dose and the total number of fully vaccinated people. No time trends were observed in the total doses administered for at least one dose of vaccine, but an apparent time trend was seen in the doses administered for the second dose. We therefore added a time trend in the estimation equation above when we conducted the sensitivity check using the total number of fully vaccinated people as our measure of vaccination. We used Eqs. ( 3 ) and ( 4 ) to estimate the infection rate and vaccination coverage, combined with the initial epidemiological data of SIR in week 1 (12 October 2020), and our model estimates of the infection rate for the following 20 weeks are highly correlated with the empirical data. For each individual state, our model estimates reached a median correlation of 99.04% (range, 86.37% to 99.95%) (Extended Data Fig.  7 ).

We assessed herd immunity based on our model estimates of the real-time basic reproduction number for each state, \({R{^{\prime}}}_{i,t}=\frac{{\upbeta }_{\mathrm{i},\mathrm{t}}{S}_{i,t}}{{\gamma }_{i}}\) ; that is, the number of cases directly caused by an infected individual throughout his or her infectious period. The model achieves herd immunity when \({R{^{\prime}}}_{i,t}\) falls below 1 in 49 states (except for Maryland and Kentucky; see Supplementary Methods for details).

For each given vaccination pace, we ran the simulation forward and projected the future dynamic of the pandemic across the U.S., assuming that no changes are made in nonpharmaceutical interventions. We then computed the time required for every state to achieve herd immunity and calculated the share of the U.S. population vaccinated when herd immunity is achieved. In addition, we conducted a sensitivity analysis regarding herd immunity with variations in vaccine effectiveness and with the addition of vaccine hesitancy. We incorporated vaccine hesitancy into our model by assuming that if x% of the population is hesitant, the cumulative vaccination coverage in each state will stop when (1 − x%) of the population is vaccinated.

Data and Code availability

The datasets and code used for the analyses are available at https://github.com/huntabaobao007/US-COVID-19-vaccination .

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Acknowledgements

We thank K.E. Warner and S. Mennemeyer for their feedback.

The Funding was provided by the National Natural Science Foundation of China (72003026), the Fundamental Research Funds for the Central Universities in UIBE (19QD01), startup grant of City University of Hong Kong (7200689), and the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 11501121).

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All authors designed the analyses, interpreted the results, and designed the figures, and are listed alphabetically. X.C., H.H., R.S., and J.Z. contribute equally to the paper. H.H. and R.S. collected the data. J.Z. conducted the reduced-form empirical analysis. X.C. conducted the analysis with the SIR model. H.H., J.J., and R.S. wrote the paper.

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Correspondence to Hanwei Huang or Ruoyan Sun .

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Chen, X., Huang, H., Ju, J. et al. Impact of vaccination on the COVID-19 pandemic in U.S. states. Sci Rep 12 , 1554 (2022). https://doi.org/10.1038/s41598-022-05498-z

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There are plenty of moral reasons to be vaccinated – but that doesn’t mean it’s your ethical duty

Director of the Master of Bioethics degree program at the Berman Institute of Bioethics, Johns Hopkins University

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With the news that all U.S. adults are now eligible to receive the COVID-19 vaccine, the holy grail of infectious disease mitigation – herd immunity – feels tantalizingly close. If enough people take the vaccine, likely at least 70% of the population, disease prevalence will slowly decline and most of us will safely get back to normal. But if not enough people get vaccinated, COVID-19 could stick around indefinitely.

The urgency of reaching that milestone has led some to claim that individuals have a civic duty or moral obligation to get vaccinated.

As a moral philosopher who has written on the nature of obligation in other contexts, I want to explore how the seemingly straightforward ethics of vaccine choice is in fact rather complex.

The simple argument

The discussion of whether or not one should take the COVID-19 vaccine is often framed in terms of individual self-interest: The benefits outweigh the risk, so you should do it.

That’s not a moral argument.

Most people likely believe that others have wide latitude in determining how they care for their own health, so it can be permissible to engage in risky activities – such as motorcycling or base jumping – even when it’s not in one’s interest. Whether one should get vaccinated, however, is a moral issue because it affects others, and in a couple of ways.

First, effective vaccines are expected to decrease not only rates of infection but also rates of virus transmission . This means that getting the vaccine can protect others from you and contribute to the population reaching herd immunity.

Second, high disease prevalence allows for more genetic mutation of a virus, which is how new variants arise. If enough people aren’t vaccinated quickly, new variants may develop that are more infectious, are more dangerous or evade current vaccines.

The straightforward ethical argument, then, says: Getting vaccinated isn’t just about you. Yes, you have the right to take risks with your own safety. But as the British philosopher John Stuart Mill argued in 1859, your freedom is limited by the harm it could do to others. In other words, you do not have the right to risk other people’s health, and so you are obligated to do your part to reduce infection and transmission rates.

It’s a plausible argument. But the case is rather more complicated.

Individual action, collective good

The first problem with the argument above is that it moves from the claim that “My freedom is limited by the harm it would cause others” to the much more contentious claim that “My freedom is limited by very small contributions my action might make to large, collective harms.”

Refusing to be vaccinated does not violate Mill’s harm principle , as it does not directly threaten some particular other with significant harm. Rather, it contributes a very small amount to a large, collective harm.

Since no individual vaccination achieves herd immunity or eliminates genetic mutation, it is natural to wonder: Could we really have a duty to make such a very small contribution to the collective good?

A version of this problem has been well explored in the climate ethics literature, since individual actions are also inadequate to address the threat of climate change. In that context, a well-known paper argues that the answer is “no”: There is simply no duty to act if your action won’t make a meaningful difference to the outcome.

Others, however, have explored a variety of ways to rescue the idea that individuals must not contribute to collective harms.

One strategy is to argue that small individual actions may actually make a difference to large collective effects, even if it’s difficult to see.

For instance: Although it appears that an individual getting vaccinated doesn’t make a significant difference to the outcome, perhaps that is just the result of uncareful moral mathematics. One’s chance of saving a life by reducing infection or transmission is very small, but saving a life is very valuable. The expected value of the outcome, then, is still high enough to justify taking it to be a moral requirement.

Another strategy concedes that individual actions don’t make a meaningful difference to large, structural problems, but this doesn’t mean morality must be silent with regard to those actions. Considerations of fairness , virtue and integrity all might recommend taking individual action toward a collective goal – even if that action did not by itself make a difference.

In addition, these and other considerations can provide reasons to act , even if they don’t imply an obligation to act.

The contours of obligation

There is yet another challenge in justifying an obligation to get vaccinated, which has to do with the very nature of obligations.

Obligations are requirements on actions, and, as such, those actions often seem demandable by members of the moral community. If a person is obligated to donate to charity, then other members of the community have the moral standing to demand a percentage of their income. That money is owed to others.

The relevant question here, then, is: Are there moral grounds to demand another person get vaccinated?

Philosopher Margaret Little has argued that very intimate actions, such as sex and gestation – the continuation of a pregnancy – are not demandable. In my own work, I’ve suggested that this is also true for deciding how to form a family – for example, adopting a child versus procreating. The intimacy of the actions, I argue, make it the case that no one is entitled to them. Someone can ask you for sex, and there are good reasons to adopt rather than procreate; but no one in the community has the moral standing to demand that you do either. These sorts of examples suggest that particularly intimate actions are not the appropriate targets of obligation.

Is getting vaccinated intimate? While it may not appear so at first blush, it involves having a substance injected into your body, which is a form of bodily intimacy. It requires allowing another to puncture the barrier between your body and the world. In fact, most medical procedures are the sort of thing that it seems inappropriate to demand of someone, as individuals have unilateral moral authority over what happens to their bodies.

The argument presented here objects to intimate duties because they seem too invasive. However, even if members of the moral community don’t have the standing to demand that others vaccinate, they are not required to stay silent; they may ask, request or entreat, based on very good reasons. And of course, no one is required to interact with those who decline.

I am certainly not trying to convince anyone that it’s OK not to get vaccinated. Indeed, the arguments throughout indicate, I think, that there is overwhelming reason to get vaccinated. But reasons – even when overwhelming – don’t constitute a duty, and they don’t make an action demandable.

Acting as though the moral case is straightforward can be alienating to those who disagree. And minimizing the moral stakes when we ask others to have a substance injected into their body can be disrespectful. A much better way, I think, is to engage others rather than demand from them, even if the force of reason ends up clearly on one side.

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Effectiveness of COVID‐19 vaccines: findings from real world studies

David a henry.

1 Institute for Evidence-Based Healthcare, Bond University, Gold Coast QLD

2 Gold Coast University Hospital and Health Service, Gold Coast QLD

Mark A Jones

3 University of Queensland, Brisbane QLD

Paulina Stehlik

Paul p glasziou.

Community‐based studies in five countries show consistent strong benefits from early rollouts of COVID‐19 vaccines

By the beginning of June 2021, almost 11% of the world’s population had received at least one dose of a coronavirus disease 2019 (COVID‐19) vaccine. 1 This represents an extraordinary scientific and logistic achievement — in 18 months, researchers, manufacturers and governments collaborated to produce and distribute vaccines that appear effective and acceptably safe in preventing COVID‐19 and its complications. 2 , 3

The initial randomised trials confirmed immunological responses and generated unbiased evidence of vaccine efficacy. They were conducted in selected populations with limited numbers of participants in high risk groups, such as older people and those with serious underlying medical conditions. 2 , 3 They provided sparse information on the impact of vaccination on transmission of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), were too small to quantify rare but serious harms, and did not take account of the logistic obstacles encountered during the community‐wide rollout of new vaccines. While large cluster randomised trials could address some of these concerns, 4 large observational studies have used large linked routinely collected population datasets in five countries to address important knowledge gaps. 5 , 6 , 7 , 8 , 9

This article reviews findings from the initial real world studies and stresses that researchers in Australia currently do not have timely access to the linked Commonwealth and state datasets needed to perform such analyses.

Real world studies

In five countries (Israel, England, Scotland, Sweden and the United States) researchers have analysed routinely collected data to report the early outcomes of community‐wide vaccination programs with three of the first vaccines to reach market: the BNT162b2 mRNA (Pfizer–BioNTech), mRNA‐1273 (Moderna) and ChAdOx1 adenoviral vector (Oxford–AstraZeneca) vaccines. 5 , 6 , 7 , 8 , 9

At the time of writing, two of the articles (from the US and Sweden ) have not yet been peer reviewed, so details reported here may change after revisions to these reports. 8 , 9 There is a rapidly growing literature on the community impact of COVID‐19 and it has provided very consistent evidence of substantial vaccine effectiveness with the original (Wuhan) viral strain and the Alpha variant. An important focus of future work will be the effectiveness of existing vaccines against emerging viral variants.

The vaccination programs against COVID‐19 commenced in December 2020 in the study countries, so follow‐up is limited. Most of the investigators used rigorous designs and statistical methods to analyse linked routinely collected person‐level data from large community‐wide databases that tracked outcomes in vaccinated and unvaccinated individuals ( Box ). Importantly, allocation to vaccines was not by randomisation, and vaccinated and unvaccinated populations differed in respect of factors that were associated with both the probability of vaccination and with the severe outcomes of COVID‐19. Information that featured in most studies included demographic details, a vaccine register, results of laboratory polymerase chain reaction (PCR) testing, records of hospitalisation and death, and some geographic measures of social deprivation. In addition, the Israeli, US and Scottish studies included linkage to clinical records from which to quantify comorbidities. 5 , 6 , 8 The Israeli study included information on previous adherence to influenza vaccination schedules. 5

Characteristics of five real world community‐based studies of effectiveness of SARS‐CoV‐2 vaccines

BNT162b2 =Pfizer–BioNTech mRNA vaccine; ChAdOx1 = Oxford–AstraZeneca adenoviral vector vaccine; mRNA‐1273 = Moderna mRNA vaccine; NHS = National Health Service; PCR = polymerase chain reaction.

Study designs and adjustments for confounding

The studies used different approaches to adjust for confounding ( Box ). The most advanced design was used to analyse the linked data from members of the Clalit Health Services integrated health care organisation in Israel, which covers around 4.7 million people. 5 The investigators extracted data on matched cohorts of vaccinees and non‐vaccinated controls and analysed study endpoints using rules that emulated the steps taken in a randomised trial. 10 These steps minimised selection or measurement biases and controlled for potential confounders through precise 1:1 matching of vaccinated and non‐vaccinated subjects across seven domains. The investigators took the additional step of calibrating their statistical model against the results of the pivotal phase 3 randomised trial, which found no benefit during the first 2 weeks after vaccination. 2 In contrast, this observational study found lower rates of infection in the first 2 weeks after vaccination, which remained after matching for age and sex — illustrating the potential for confounding. Only after full matching on seven factors was this source of bias eliminated. 5

In England, investigators linked data from a national vaccine register to laboratory PCR swab results, emergency department admissions, demographic and ethnicity data, care home status, and deaths in participants aged 70 years and over ( Box ). 7 The first part was a test‐negative case–control design, which compared vaccination status in those who received a positive PCR swab result with contemporaneous controls who returned a negative result. That both cases and controls had been tested for SARS‐CoV‐2 should have controlled for clinical and behavioural factors that influence the probability of having a test. The second part of the study followed participants aged 80 years and over with a positive PCR test result and analysed them according to vaccination status. The investigators calculated adjusted hazard ratios for death up to and beyond 14 days from the first vaccine dose.

A study in Scotland used an unmatched cohort design comparing hospital admission for COVID‐19 in people who received either the Pfizer–BioNTech or Oxford–AstraZeneca vaccines with an unvaccinated control group. 6 The Oxford–AstraZeneca vaccine was given later to an older population. The study adjusted for age and sex, frequency of prior PCR tests and clinical risk groups extracted from linked health records. The statistical model generated unexpectedly strong protective effects of the vaccines on hospitalisation rates in the first 2 weeks after vaccination, indicating possible bias due to a healthy vaccinee effect.

In the US, researchers working within the Mayo Clinic health system used postcode and propensity scores (based on age, sex, race, ethnicity and records of PCR testing) to match a cohort of individuals who received the Pfizer–BioNTech or Moderna mRNA vaccine with unvaccinated controls, to measure impact on infections and hospitalisations. 8

A simple unmatched cohort design using linkage of routinely collected administrative data measured infection rates in a cohort who received the Pfizer–BioNTech vaccine in a single county in Sweden. 9 The unvaccinated population acted as controls ( Box ). Confounding adjustments in this study were limited to age and sex.

The Box summarises the results of these studies. All included at least one mRNA vaccine and the reductions in infections and hospitalisations were consistent and large. Two studies reported on mortality and the reductions were substantial, although based on small numbers of deaths in Israel. 5 , 7 The studies did not directly compare vaccines, but the Oxford–AstraZeneca vaccine appeared to perform as well as the mRNA vaccines in reducing hospitalisations.

Other approaches to estimating vaccine effectiveness

In the UK, over 600 000 volunteers using a COVID‐19 symptom mobile phone app recorded adverse events after vaccination with either the Pfizer–BioNTech or Oxford–AstraZeneca vaccine. 11 Based on post‐vaccination self‐reports of infections and after adjustment for age, sex, obesity and comorbidities, they estimated effectiveness rates of 60–70% beyond 21 days after administration of either vaccine.

Three studies measured the effectiveness of COVID‐19 vaccines in care home, health care and other frontline workers in the UK, Israel and the US. 12 , 13 , 14 These projects enrolled smaller numbers of participants than the community‐based studies but used similar designs and adjustment techniques. Importantly, workers in these settings undergo routine PCR testing for SARS‐CoV‐2, which enabled detection of asymptomatic infections. These studies also found large protective effects and a potential to reduce viral transmission. The latter possibility has been investigated directly in a study conducted in Scotland that showed that 14 days or more after health care workers received a second dose of vaccine, their household members had a 54% lower rate of COVID‐19 than individuals who shared households with non‐vaccinated health care workers. 15

Conclusions

We can draw important conclusions from these non‐randomised studies of vaccine effectiveness. Most importantly, the currently available COVID‐19 vaccines appear to be very effective in preventing severe complications and deaths from COVID‐19 in adults of all ages. Recent real world studies confirm that substantial protection extends to the Delta variant of SARS‐CoV‐2, although this requires two vaccine doses. 16 , 17 Follow‐up periods in all studies are relatively short, and these reports do not provide information on rare but serious adverse events, such as cerebral venous thrombosis. The use of sophisticated trial emulation methods in the Israeli study 5 replicated some key features of the pivotal randomised trial of the Pfizer–BioNTech vaccine, 2 particularly by controlling for an early healthy cohort effect that confounded the incompletely adjusted endpoint analyses. This design should prove useful in enabling direct head‐to‐head comparisons of effectiveness and safety of vaccines, the duration of their protective effects, the degree to which vaccines prevent transmission of viral variants, and the impact of vaccines on so‐called long COVID.

These studies exemplify the value of advanced analyses of large multiply linked routinely collected community datasets. This resource is not yet readily available to researchers in Australia due to continued lack of agreement on the governance of linked state and Commonwealth datasets. 18 While Australia’s current low rates of community transmission of SARS‐CoV‐2 reduce the feasibility of observational studies of vaccine effectiveness, the available data can provide important information on potential harms of vaccines. With continuing questions about the comparative safety of vaccines, the emergence of viral variants, the long term effects of COVID‐19 and the likelihood of future epidemics, it is essential that Australia urgently removes barriers to allowing prequalified researchers to safely access the linked de‐identified population datasets that are needed to expeditiously conduct the types of studies reviewed here.

Competing interests

No relevant disclosures.

Not commissioned; externally peer reviewed.

The unedited version of this article was published as a preprint on mja.com.au on 20 May 2021.

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FDA Approves and Authorizes Updated mRNA COVID-19 Vaccines to Better Protect Against Currently Circulating Variants

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Today, the U.S. Food and Drug Administration approved and granted emergency use authorization (EUA) for updated mRNA COVID-19 vaccines (2024-2025 formula) to include a monovalent (single) component that corresponds to the Omicron variant KP.2 strain of SARS-CoV-2. The mRNA COVID-19 vaccines have been updated with this formula to more closely target currently circulating variants and provide better protection against serious consequences of COVID-19, including hospitalization and death. Today’s actions relate to updated mRNA COVID-19 vaccines manufactured by ModernaTX Inc. and Pfizer Inc.

In early June, the FDA advised manufacturers of licensed and authorized COVID-19 vaccines that the COVID-19 vaccines (2024-2025 formula) should be monovalent JN.1 vaccines. Based on the further evolution of SARS-CoV-2 and a rise in cases of COVID-19, the agency subsequently determined and advised manufacturers that the preferred JN.1-lineage for the COVID-19 vaccines (2024-2025 formula) is the KP.2 strain, if feasible.

“Vaccination continues to be the cornerstone of COVID-19 prevention,” said Peter Marks, M.D., Ph.D., director of the FDA’s Center for Biologics Evaluation and Research. “These updated vaccines meet the agency’s rigorous, scientific standards for safety, effectiveness, and manufacturing quality. 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.”

The updated mRNA COVID-19 vaccines include Comirnaty and Spikevax, both of which are approved for individuals 12 years of age and older, and the Moderna COVID-19 Vaccine and Pfizer-BioNTech COVID-19 Vaccine, both of which are authorized for emergency use for individuals 6 months through 11 years of age.

What You Need to Know

• Unvaccinated individuals 6 months through 4 years of age are eligible to receive three doses of the updated, authorized Pfizer-BioNTech COVID-19 Vaccine or two doses of the updated, authorized Moderna COVID-19 Vaccine.
• Individuals 6 months through 4 years of age who have previously been vaccinated against COVID-19 are eligible to receive one or two doses of the updated, authorized Moderna or Pfizer-BioNTech COVID-19 vaccines (timing and number of doses to administer depends on the previous COVID-19 vaccine received).
• Individuals 5 years through 11 years of age regardless of previous vaccination are eligible to receive a single dose of the updated, authorized Moderna or Pfizer-BioNTech COVID-19 vaccines; if previously vaccinated, the dose is administered at least 2 months after the last dose of any COVID-19 vaccine.
• Individuals 12 years of age and older are eligible to receive a single dose of the updated, approved Comirnaty or the updated, approved Spikevax; if previously vaccinated, the dose is administered at least 2 months since the last dose of any COVID-19 vaccine.
• Additional doses are authorized for certain immunocompromised individuals ages 6 months through 11 years of age as described in the Moderna COVID-19 Vaccine and Pfizer-BioNTech COVID-19 Vaccine fact sheets.

Individuals who receive an updated mRNA COVID-19 vaccine may experience similar side effects as those reported by individuals who previously received mRNA COVID-19 vaccines and as described in the respective prescribing information or fact sheets. The updated vaccines are expected to provide protection against COVID-19 caused by the currently circulating variants. Barring the emergence of a markedly more infectious variant of SARS-CoV-2, the FDA anticipates that the composition of COVID-19 vaccines will need to be assessed annually, as occurs for seasonal influenza vaccines.

For today’s approvals and authorizations of the mRNA COVID-19 vaccines, the FDA assessed manufacturing and nonclinical data to support the change to include the 2024-2025 formula in the mRNA COVID-19 vaccines. The updated mRNA vaccines are manufactured using a similar process as previous formulas of these vaccines. The mRNA COVID-19 vaccines have been administered to hundreds of millions of people in the U.S., and the benefits of these vaccines continue to outweigh their risks.

On an ongoing basis, the FDA will review any additional COVID-19 vaccine applications submitted to the agency and take appropriate regulatory action.

The approval of Comirnaty (COVID-19 Vaccine, mRNA) (2024-2025 Formula) was granted to BioNTech Manufacturing GmbH. The EUA amendment for the Pfizer-BioNTech COVID-19 Vaccine (2024-2025 Formula) was issued to Pfizer Inc.

The approval of Spikevax (COVID-19 Vaccine, mRNA) (2024-2025 Formula) was granted to ModernaTX Inc. and the EUA amendment for the Moderna COVID-19 Vaccine (2024-2025 Formula) was issued to ModernaTX Inc.

Related Information

• Comirnaty (COVID-19 Vaccine, mRNA) (2024-2025 Formula)
• Spikevax (COVID-19 Vaccine, mRNA) (2024-2025 Formula)
• Moderna COVID-19 Vaccine (2024-2025 Formula)
• Pfizer-BioNTech COVID-19 Vaccine (2024-2025 Formula)
• FDA Resources for the Fall Respiratory Illness Season
• Updated COVID-19 Vaccines for Use in the United States Beginning in Fall 2024
• June 5, 2024, Meeting of the Vaccines and Related Biological Products Advisory Committee

The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nation’s food supply, cosmetics, dietary supplements, radiation-emitting electronic products, and for regulating tobacco products.

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Side Effects of COVID-19 Vaccines

This article is part of a series of explainers on vaccine development and distribution. Learn more about vaccines – from how they work and how they’re made to ensuring safety and equitable access – in WHO’s Vaccines Explained series.

COVID-19 vaccines are safe, and getting vaccinated will help protect you against developing severe COVID-19 disease and dying from COVID-19. You may experience some mild side effects after getting vaccinated, which are signs that your body is building protection.

Why it’s normal to have mild side effects from vaccines

Vaccines are designed to give you immunity without the dangers of getting the disease. It’s common to experience some mild-to-moderate side effects when receiving vaccinations. This is because your immune system is instructing your body to react in certain ways: it increases blood flow so more immune cells can circulate, and it raises your body temperature in order to kill the virus.

Mild-to-moderate side effects, like a low-grade fever or muscle aches, are normal and not a cause for alarm: they are signs that the body’s immune system is responding to the vaccine, specifically the antigen (a substance that triggers an immune response), and is gearing up to fight the virus. These side effects usually go away on their own after a few days.

Common and mild or moderate side effects are a good thing: they show us that the vaccine is working. Experiencing no side effects doesn’t mean the vaccine is ineffective. It means everybody responds differently. 

Common side effects of COVID-19 vaccines

Like any vaccine, COVID-19 vaccines can cause side effects, most of which are mild or moderate and go away within a few days on their own. As shown in the results of clinical trials, more serious or long-lasting side effects are possible. Vaccines are continually monitored to detect adverse events.

Reported side effects of COVID-19 vaccines have mostly been mild to moderate and have lasted no longer thana few days. Typical side effects include pain at the injection site, fever, fatigue, headache, muscle pain, chills and diarrhoea. The chances of any of these side effects occurring after vaccination differ according to the specific vaccine.

COVID-19 vaccines protect against the SARS-CoV-2 virus only, so it’s still important to keep yourself healthy and well.

Less common side effects

Upon receiving the vaccine, a person should be requested to stay for 15–30 minutes at the vaccination site so health workers are available in case of any immediate reactions. Individuals should alert their local health providers following vaccination if they experience any unexpected side effects or other health events – such as side effects lasting more than three days. Less common side effects reported for some COVID-19 vaccines have included severe allergic reactions such as anaphylaxis; however, this reaction is extremely rare. 

National authorities and international bodies, including WHO, are closely monitoring for any unexpected side effects following COVID-19 vaccine use.

Long-term side effects

Side effects usually occur within the first few days of getting a vaccine. Since the first mass vaccination programme started in early December 2020, hundreds of millions of vaccine doses have been administered.

There have been concerns about COVID-19 vaccines making people sick with COVID-19. But none of the approved vaccines contain the live virus that causes COVID-19, which means that COVID-19 vaccines cannot make you sick with COVID-19.

After vaccination, it usually takes a few weeks for the body to build immunity against SARS-CoV-2, the virus that causes COVID-19. So it’s possible a person could be infected with SARS-CoV-2 just before or after vaccination and still get sick with COVID-19. This is because the vaccine has not yet had enough time to provide protection.

Experiencing side effects after getting vaccinated means the vaccine is working and your immune system is responding as it should. Vaccines are safe, and getting vaccinated will help protect you against COVID-19.

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No evidence of increased incidence of ANCA-associated vasculitis following the COVID-19 pandemic: a single-centre experience

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Elia Touma, Naiel Bisharat, No evidence of increased incidence of ANCA-associated vasculitis following the COVID-19 pandemic: a single-centre experience, Rheumatology , Volume 63, Issue 9, September 2024, Pages e256–e257, https://doi.org/10.1093/rheumatology/keae115

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Dear editor , Over the past few years, researchers have been intrigued by the potential link between coronavirus disease 2019 (COVID-19), COVID-19 vaccines and vasculitis [ 1–3]. Specifically, few studies have reported occurrences of ANCA-associated vasculitis (AAV) following vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [ 4] or SARS-CoV-2 infection [ 5–7], prompting speculation about a causal relationship between COVID-19, or COVID-19 vaccinations, and AAV. In this context, a recent study from Japan has reported a statistically significant increase in the annual number of newly diagnosed cases of AAV in Nagasaki Prefecture (Southern Japan) since the initiation of the COVID-19 vaccination program [ 8].

Since the beginning of the COVID-19 pandemic, there has been a noted rise in newly identified cases of AAV in Israel, as informally reported by rheumatologists nationwide (N.Bisharat, unpublished data). Our objective was to determine the incidence of newly assigned cases of AAV since the beginning of the pandemic and compare it with the pre-pandemic period. To address this, we utilized nationally collected databases from Clalit Health Services to study the incidence of AAV from before the COVID-19 pandemic (2002–19) and throughout the pandemic to the end of 2022. Clalit Health Services is the largest healthcare provider in Israel, providing medical care for more than 5 million citizens. Newly diagnosed cases of AAV were extracted from the database spanning the years 2000–22, utilizing appropriate International Classification of Diseases, Ninth Revision codes. Additionally, all confirmed cases of SARS-CoV-2 infection, verified through positive PCR or certified rapid antigen tests, were retrieved, irrespective of the purpose of testing.

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How, where and when to get updated COVID booster shots

The Centers for Disease Control and Prevention  recommended Thursday   that teenagers and adults get updated booster shots from Pfizer or Moderna. The shots — also known as bivalent vaccines —are designed to target both the original coronavirus strain and the currently circulating omicron subvariants BA.4 and BA.5 .

The decision follows a similar recommendation from a panel of independent advisers to the CDC, which voted in favor of the shots Thursday.

The CDC’s recommendation means the shots can now be administered to the public. But a spokesperson for the Department of Health and Human Services said people likely won’t start getting updated boosters until after Labor Day.

After that, appointment availability is expected to ramp up over several days, with appointments becoming more broadly available in a few weeks, a senior administration official said. People will be able to search for the closest sites offering updated boosters at  Vaccines.gov .

Here’s what to know about the updated shots.

Are there enough doses for everyone?

White House officials said vaccine supply should meet demand this fall. The administration has purchased 171 million updated booster doses — 105 million from Pfizer and 66 million from Moderna — thus far, with the option to procure up to 429 million more.

Distribution of the doses began after the Food and Drug Administration  authorized the shots  Wednesday, with shipments to tens of thousands of locations, including pharmacies. Before that, pharmacies, community health centers and rural health clinics could pre-order the shots from the federal government.

A CVS spokesperson said its pharmacies expect to get updated booster doses on a rolling basis over the next few days. People can make appointments as usual on CVS’ website or its app.

Walgreens similarly said people can make appointments to get updated boosters through its website or its app or over the phone.

For now, the shots remain free.

How are these boosters different?

Whereas the initial COVID vaccine boosters targeted only the original strain of the coronavirus, the updated boosters are designed to add protection against omicron subvariants. For that reason, the modified shots will be the only boosters available for teens and adults moving forward.

The newly authorized shots target the BA.4 and BA.5 subvariants. As of Tuesday, BA.5 accounted for at least 87% of new U.S. cases. BA.4 and a similar sublineage, BA.4.6, made up around 11%.

“The updated COVID-19 boosters are formulated to better protect against the most recently circulating COVID-19 variant. They can help restore protection that has waned since previous vaccination and were designed to provide broader protection against newer variants,” CDC Director Dr. Rochelle Walensky said in a statement on Thursday.

Pfizer’s and Moderna’s trials of their bivalent vaccines in people studied a formulation that targeted the original omicron strain. The updated version, however, was tested in laboratory studies, which found that the boosters generated  strong antibody responses  against BA.4 and BA.5.

Laboratory tests “so far have been a very good predictor of how well the vaccines protect against infection, as well as protecting against severe disease and hospitalization and death,” said David Montefiori, a professor at the Human Vaccine Institute at Duke University Medical Center.

Who should get a booster?

The FDA authorized Pfizer’s shot for people ages 12 and up and Moderna’s for ages 18 and up. For those who are  up to date on their COVID vaccinations , the updated booster constitutes a fourth, fifth, or sixth shot, depending on one’s age and health status.

But some vaccine experts wonder whether the shots are necessary yet for young, healthy people, given the lack of clinical trial data to demonstrate how well they work against the newer omicron subvariants.

Nonetheless, Pablo Penaloza-MacMaster, an assistant professor of microbiology-immunology at Northwestern University, said the potential benefits seem to outweigh the risks.

“The way that I look at it right now is that it seems like there’s not much to lose,” he said.

When is the ideal timing for this booster?

The CDC suggests   that people wait at least two months since their most recent COVID shots to get the latest shots.

People who are elderly or immunocompromised should get boosted as soon as they meet those qualifications, Montefiori said. But he suggested that there’s likely to be more wiggle room in the timing for young, healthy people.

“The longer you wait to get the boost, the more potent of a boosting effect it’s going to have,” he said. But for those who hold off, he added, “there’s that trade-off between waiting to get boosted so that you have a stronger boosting effect and the risk of getting infected while you’re waiting to get the boost.”

Montefiori, who is 68, said he got his fifth shot three weeks ago and plans to wait three months for his bivalent booster.

The  CDC advises  that people who recently had COVID consider delaying their boosters until three months after their symptoms started or, if they were asymptomatic, since their positive COVID tests.

Penaloza-MacMaster said  his research suggests  that healthy people of all ages could even wait six months between shots or following COVID infections.

But Montefiori said it’s hard to know how long immune protection lasts after a COVID infection.

“The best advice that I would give people is to get the bivalent boost as soon as they’re eligible to, regardless of whether or not they’ve been infected, because of the uncertain nature of how much that infection really boosted your immunity,” he said.

This story originally appeared on NBCNews.com .

Aria Bendix is the breaking health reporter for NBC News Digital.

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