preparedness hypothesis definition psychology

Biological Preparedness Theory In Psychology

Charlotte Nickerson

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Key Takeaways

• Biological preparedness postulates that organisms are evolutionarily predisposed to developing associations between certain stimuli and responses. For example, people can be more predisposed to fearing things (such as heights or snakes) that have historically presented a mortal threat to humans.
• Martin Seligman proposed preparedness theory to explain fears and phobias as providing an evolutionary advantage and, therefore, being passed on by natural selection. Although biological preparedness spawned from research examining fear, researchers have since used the theory to explain phenomena ranging from taste aversion to industrial design.
• Following criticism of the original preparedness theory experiments, theorists have created several theories that attempt to explain how people form fears around stimuli that did not historically threaten humans.

Definition and Background

Biological preparedness is the idea that organisms are biologically predisposed to quickly learning associations between stimuli, responses, and reinforcers (Seligman, 1971).

This quick-learning can be explained by an organism’s fit with genetic traits that evolved to increase the species’ chances of survival. For example, people may be averse to certain foods associated with gastrointestinal illness — such as rotting meat — even if they have never eaten them before.

Seligman suggests that humans have a biological preparedness to develop certain phobias rather than others because they were adaptive (i.e., helpful) in our evolutionary past.

For example, individuals who avoided snakes and high places would be more likely to survive long enough and pass on their genes than those who did not.

Seligman’s Preparedness theory is one of the most influential ideas in explaining the existence of particular phobias (Åhs et al., 2018).

Martin E. P. Seligman (1971) proposed the preparedness theory of fears and phobias. Seligmana’s article, Phobias and Preparedness , represented a break from traditional conditioning theories of fear and inspired a line of research integrating evolutionary theory with learning theory, which continues to this day (McNally, 2015).

This research has spawned adjacent theories of selective sensitization, expectancy, and nonassociative theories.

Seligman trained as an experimental psychologist specializing in animal learning and motivation. However, he soon acquired clinical training, which expanded his reach to psychopathology.

Perhaps his best-known contributions are the learned helplessness theory of depression (Seligman, 1975) and his work in the field of positive psychology (Seligman and Csikszentmihalyi, 2000).

Seligman used preparedness theory to make phenomena that traditional conditioning theories could not explain or test testable (McNally, 2015).

Prior to Seligman’s work, there was a growing emphasis on phylogenetics — the study of evolutionary relationships between species — in learning and conditioning research.

In particular, John Garcia’s taste aversion research found that rats rapidly learn to avoid sweetened water when its ingestion is accompanied by radiation-induced gastric district, but not with a food stock.

Indeed, Garcia and Koelling (1967) used “natural selection” to explain why taste cues could be easily associated with gastrointestinal distress, while audiovisual cues were more likely to be associated with footshock-induced pain (McNally, 2015).

Seligman would adopt Garcia and Koelling’s evolutionary invocation to argue that evolution shapes organisms’ propensity for learning certain behaviors over others.

Seligman’s article would go on to have a major impact on anxiety disorders, and in the years following Seligman’s publication, laboratory experiments testing preparedness theory began to appear.

Biological Preparedness Working With Classical Conditioning

The most prominent psychophysiologist to experiment with preparedness theory was the Swede Arne Öhnman.

Öhnman conducted a series of classical conditioning experiences where he attempted to facilitate participants in learning and unlearning fears associated with colored photographs of stimuli, which Öhnman believed people would be biologically prepared to acquire fear-eliciting properties too.

For example, snakes, spiders, and men with angry facial expressions. Öhnman showed one group of people a series of fear-relevant stimuli and another group fear-irrelevant stimuli, such as pictures of flowers or circles and triangles.

At random, he assigned one picture category so that participants would receive an “uncomfortable, but not painful” electric shock whenever they saw it (McNally, 2015).

Öhnman associated increased skin conductivity with increased fear, as it reflects sweating in the palm of the hand.

Öhnman found that, while people acquired fears of the non-fear stimulating and fear-stimulating photos at a similar rate, fear of the fear-stimulating photos persisted long after each set of photos was no longer associated with an electric shock.

In fact, even when the researchers instructed participants that no further shocks would happen and physically removed the shock electrodes, fears of the fear-relevant images still persisted.

Åhs etc. al. (2018) conducted a systematic review of 32 fear conditioning experiments. In these experiments, researchers attempted to decondition participants’ fear of spiders and snakes by showing them a series of pictures.

22 of these 32 experiments were able to decondition the fear of spiders, which Åhs et al. argue weakens preparedness theory as an explanation for fear.

Other studies have shown that instructions can quickly diminish fear responses to fear-relevant stimuli (Dawson, Schell, and Tweddle-Banis, 1986), and others have failed to replicate the resistance to fear deconditioning described in Ohman’s original study (McNally and Foa, 1986).

Studies such as McNally and Reiss (1982) have also shown that people can be conditioned to believe that fear-related stimuli are non-shock safety signals just as easily as they can with non-fear-related stimuli.

All in all, studies have shown deeply ambivalent empirical support for preparedness theory. Scholars have also questioned how Seligman (1971) interpreted phobias.

For example, noting that rather than having acquired fears through Pavlovian conditioning , many people have recounted having feared stimuli, such as snakes, for as long as they can remember.

Rather than remembering experiencing active harm from the stimulus — such as being bitten by a snake — those who cannot remember the onset of their fear report experiencing intense fear when encountering the stimulus.

In addition, researchers have questioned why some people develop fears but not others. For example, dog bites and falls from high places occur as frequently in the past of those who do not have intense fears of dogs and heights as those who do (Di Nardo et al., 1988; Menzies and Parker, 2001).

There have also been questions about the origins of fears related to modern technology, for example, whether or not the fear of flying on airplanes originates from a biologically prepared height phobia or contemporary society (McNally and Louro, 1992).

Critical Evaluation

Jeffrey Gray (1987) wrote a notable critique of preparedness and Öhnman’s experiments. This critique had three main points:

• The slower deconditioning to fear-related stimuli that Ohman demonstrated was a result of participants being better able to discriminate between fear-related stimuli that provided or did not provide shocks than non-fear-related stimuli.

Gray argued that the preparedness effect would actually entail that there would be larger responses overall to fear-related stimuli that provided shocks than those irrelevant to fear.

• Secondly, Gray argued, the participants in Ohman’s experiment did not necessarily have a heightened fear response but were better able to orient themselves according to the fear-related pictures.
• Lastly, Gray cited research where participants experienced greater fear responses to fear-relevant stimuli than non-relevant stimuli when they were told that they might receive shocks that never occurred (Öhnman et al., 1974). From this, Gray hypothesized that Ohman’s experiments did not test Pavlovian conditioning but selective sensitization to fear cues. Lovibond, Siddle, and Bond’s (1993) experiment subsequently bolstered this supposed selective sensitization effect (McNally, 2015).

Expectancy Theory, Nonassociative Theory, and Covariation Bias

Following Gray’s critique, scientists pivoted their focus from the role of Pavlovian conditioning in fear formation to theories consistent with Grey’s selective sensitization. The three most notable of these pilots were expectancy theory, nonassociative theory, and covariation bias.

Expectancy theory, as formulated by Davey (1992), holds that ontogenetic, or cultural factors, shape expectations of which stimuli are likely to be associated with adverse events (such as shocks) in the laboratory.

Consistent with biological preparedness, snakes and spiders incited larger fear responses than, say, flowers. However, guns only elicited a higher fear response when the picture showed it pointed at the participant.

Davey’s work argues that evolution and social expectations play into how people develop fear responses (McNally, 2015).

Nonassociative theory maintains an emphasis on evolution (Menzies and Clarke, 1995).

According to nonassociative theory, people respond, particularly during childhood, to different stimuli with different amounts of fear depending on the threats that these stimuli have provided throughout history (for example, snakes and heights) (McNally, 2015).

These fears are nonassociative because they do not require painful experiences with stimuli. Meenzies and other theorists claimed that nonassociation is one of four ways that people develop fears.

The others are Pavlovian conditioning, observational learning, and verbal transmission of threatening information (Rachman, 1977).

Lastly, covariation bias sprung out of researchers such as Tomarken, Mineka, and Cook, who found that people were more likely to associate pictures of common fear stimuli with negative outcomes in the lab.

However, a further study showed that this only seemed to apply to phylogenetic (evolutionarily-backed) fear-stimuli and not ontogenetic (man-made) stimuli (Tomarken, Sutton, and Mineka, 1995).

One of the most notable lines of research in biological preparedness is taste aversion. Biological preparedness argues that organisms are more likely to become averse to foods traditionally associated with sickness and gastrointestinal distress.

Besides Garcia and Knoelling’s (1966) study on how sweetened water inspired taste aversion in rats when paired with radiation-induced gastrointestinal distress, other researchers have researched the effect.

For example, Bernstein and Webster (1980) examined learned taste aversion in humans. The researchers exposed adults receiving chemotherapy to one of two distinct, novelty-flavored ice creams and found that these participants favored the ice cream flavor they received far less than the other in subsequent experiments.

Previously, Bernstein had conducted a similar experiment on children receiving a treatment that would induce nausea and vomiting (1982). Bernstein and Webster explained this phenomenon along evolutionary lines.

Purucker, Sprott, and Herrmann (2014) elicited biological preparedness in investigating how participants reacted to the design of car fronts.

The researchers showed participants pictures of car fronts designed to appear anthropomorphically threatening (such as those resembling an angry human face) and measured responses through eye-tracking.

Ultimately, the researchers found that automotive stimuli both elicited self-reported affective responses and led to automatic responses “explained by evolutionary theory.”

In line with the so-called “threat advantage effect,” for example, the researchers showed that threateningly designed cars initially drew the attention of participants but tended to be ignored over time.

Åhs, F., Rosén, J., Kastrati, G., Fredrikson, M., Agren, T., & Lundström, J. N. (2018). Biological preparedness and resistance to extinction of skin conductance responses conditioned to fear relevant animal pictures: A systematic review. Neuroscience & Biobehavioral Reviews, 95, 430-437.

Bernstein, I. L., & Webster, M. M. (1980). Learned taste aversions in humans. Physiology & Behavior, 25(3), 363-366.

Bernstein, I. L., Webster, M. M., & Bernstein, I. D. (1982). Food aversions in children receiving chemotherapy for cancer. Cancer, 50(12), 2961-2963.

Csikszentmihalyi, M., & Seligman, M. (2000). Positive psychology. American Psychologist, 55(1), 5-14.

Davey, G. C. (1992). Classical conditioning and the acquisition of human fears and phobias: A review and synthesis of the literature. Advances in Behaviour Research and Therapy, 14(1), 29-66.

Dawson, M. E., Schell, A. M., & Banis, H. T. (1986). Greater resistance to extinction of electrodermal responses conditioned to potentially phobic CSs: A noncognitive process? Psychophysiology, 23(5), 552-561.

Di Nardo, P. A., Guzy, L. T., Jenkins, J. A., Bak, R. M., Tomasi, S. F., & Copland, M. (1988). Etiology and maintenance of dog fears. Behaviour Research and Therapy, 26(3), 241-244.

Foa, E. B., & McNally, R. J. (1986). Sensitivity to feared stimuli in obsessive-compulsives: A dichotic listening analysis. Cognitive therapy and research, 10(4), 477-485.

Garcia, J., & Koelling, R. A. (1967). A comparison of aversions induced by X rays, toxins, and drugs in the rat. Radiation Research Supplement, 7, 439-450.

Gray, J. A. (1987). Perspectives on anxiety and impulsivity: A commentary.

Maturski, E. J., Bond, N. W., Siddle, D. A., & Lovibond, P. F. (1993). Classical Conditioning of Autonomic and Affective Responses to Fear‐Relevant and Fear‐Irrelevant Stimuli. Australian journal of psychology, 45(2), 69-73.

McNally, R. J. (2016). The legacy of Seligman’s” phobias and preparedness”(1971). Behavior therapy, 47(5), 585-594.

McNally, R. J., & Reiss, S. (1982). The preparedness theory of phobias and human safety-signal conditioning. Behaviour Research and Therapy, 20(2), 153-159.

• Menzies, R. G., & Clarke, J. C. (1995). The etiology of phobias: A nonassociative account. Clinical Psychology Review, 15(1), 23-48.

Menzies, R. G., & Parker, L. (2001). The origins of height fear: an evaluation of neoconditioning explanations. Behaviour Research and Therapy, 39(2), 185-199.

Purucker, C., Sprott, D. E., & Herrmann, A. (2014). Consumer response to car fronts: eliciting biological preparedness with product design. Review of Managerial Science, 8(4), 523-540.

Rachman, S. (1977). The conditioning theory of fearacquisition: A critical examination. Behaviour Research and Therapy, 15(5), 375-387.

Seligman, M. E. (1971). Phobias and preparedness. Behavior therapy, 2(3), 307-320.

Tomarken, A. J., Sutton, S. K., & Mineka, S. (1995). Fear-relevant illusory correlations: What types of associations promote judgmental bias? Journal of abnormal psychology, 104(2), 312.

Further Information

• Öhman, A., & Mineka, S. (2001). Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychological review, 108(3), 483.
• Poulton, R., & Menzies, R. G. (2002). Fears born and bred: toward a more inclusive theory of fear acquisition. Behaviour Research and Therapy, 40(2), 197-208.
• Öhman, A., Soares, S. C., Juth, P., Lindström, B., & Esteves, F. (2012). Evolutionary derived modulations of attention to two common fear stimuli: Serpents and hostile humans. Journal of Cognitive Psychology, 24(1), 17-32.
• Mineka, S., & Öhman, A. (2002). Born to fear: non-associative vs associative factors in the etiology of phobias. Behaviour research and therapy, 40(2), 173-184.

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Biological Preparedness and Classical Conditioning

Biological preparedness is the idea that people and animals are inherently inclined to form associations between certain stimuli and responses. This concept plays an important role in learning, particularly in understanding the classical conditioning process .

Some associations form easily because we are predisposed to form such connections, while other associations are much more difficult to form because we are not naturally predisposed to them.

It has been suggested that biological preparedness explains why certain types of phobias tend to form more easily. For example, we tend to develop a fear of things that may pose a threat to our survival, such as heights, spiders, and snakes. Those who learned to fear such dangers more readily were more likely to survive and reproduce.

Biological Preparedness Working With Classical Conditioning

One example of biological preparedness in the classical conditioning process is the development of taste aversions . Have you ever eaten something and immediately become sick afterward? If so, chances are high you avoided eating that particular food in the future, even if the food did not caused your illness.

So why do we form associations between the taste of food and illness so easily? We could just as easily form such associations between people who were present when we became ill, the location of the illness, or specific objects that were present.

Biological preparedness is the key.

People (and animals) are innately predisposed to form associations between tastes and illness. Why? It is most likely due to the evolution of survival mechanisms.

Species that readily form such associations between food and illness are more likely to avoid those foods again in the future, thus ensuring their chances for survival and the likelihood that they will reproduce.

Many phobia objects involve things that potentially pose a threat to safety and well-being. Snakes, spiders, and dangerous heights are all things that can potentially be deadly. Biological preparedness makes it so that people tend to form fear associations with these threatening options. Because of that fear, people tend to avoid those possible dangers, making it more likely that they will survive. Since these people are more likely to survive, they are also more likely to have children and pass down the genes that contribute to such fear responses.

Seligman ME. Phobias and Preparedness - Republished Article .  Behav Ther . 2016;47(5):577‐584. doi:10.1016/j.beth.2016.08.006

Chambers KC. Conditioned taste aversions .  World J Otorhinolaryngol Head Neck Surg . 2018;4(1):92‐100. doi:10.1016/j.wjorl.2018.02.003

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Seligman's Preparedness Theory

Preparedness: Psychology Definition, History & Examples

Preparedness in psychology refers to the predisposition of individuals or groups to anticipate and respond effectively to certain stimuli or emergencies.

This concept has its roots in evolutionary theory , wherein certain behaviors are understood to have been advantageous for survival, thereby becoming innate responses to particular cues in the environment .

Over time, research has expanded the understanding of preparedness to include not only instinctual reactions but also learned behaviors through classical and operant conditioning .

Examples of psychological preparedness range from the rapid acquisition of phobias to societal training for disaster response.

This brief overview will delve into the intricate definition of preparedness, trace its historical development within psychological study, and present real-world instances that illustrate its practical application.

Additionally, related terms and seminal references will be discussed to provide a comprehensive understanding of the concept.

Table of Contents

Preparedness, in psychology, refers to our natural tendency to develop certain behaviors or responses when we encounter specific situations or stimuli. It suggests that we are instinctively wired to learn and react in ways that have helped us survive and thrive throughout human evolution.

Understanding preparedness helps us empathize with and comprehend why certain fears or inclinations are deeply rooted in our biology rather than being irrational. This knowledge is important for designing effective therapies and educational approaches.

The concept of psychological preparedness originated in the early 20th century and has since developed significantly through the work of pioneering behaviorists and evolutionary psychologists. It emerged as a result of studying how individuals and societies anticipate and respond to potential threats, leading to a deeper understanding of the mechanisms behind human adaptability.

One key figure associated with the development of psychological preparedness is John B. Watson, an influential behaviorist who believed that behavior is primarily shaped by conditioning and learning . Watson argued that humans are biologically prepared to fear certain things, such as snakes and spiders, due to their evolutionary significance in terms of survival and reproductive success.

Another important theorist in the field of psychological preparedness is Martin Seligman, a prominent figure in the field of positive psychology. Seligman proposed the concept of preparedness as an evolved trait that helps individuals respond adaptively to potential threats. He suggested that humans have a predisposition to quickly learn associations between certain stimuli and negative outcomes, which aids in survival and avoiding harm.

Significant events and studies have contributed to the evolution of the concept of psychological preparedness. For example, the famous Little Albert experiment conducted by Watson and his colleague Rosalie Rayner in 1920 demonstrated how fear can be conditioned in a young child. This study provided evidence for the idea that fear responses can be learned and that certain stimuli are more readily associated with fear.

In more recent years, research on post-traumatic stress disorder (PTSD) has further emphasized the importance of psychological preparedness. Studies have shown that individuals who have experienced trauma are more likely to develop PTSD if they have a preexisting vulnerability or predisposition to fear-related responses.

• Test Anxiety : Imagine a student preparing for a big exam. They may experience test anxiety, which is a psychological term referring to the stress and worry that can interfere with their performance . This can manifest as racing thoughts, physical tension, and difficulty concentrating. To overcome test anxiety, the student might employ relaxation techniques such as deep breathing or positive self-talk to calm their nerves and improve their focus. By understanding and addressing their test anxiety, the student can perform better on exams.
• Impulse Control: Consider a person trying to stick to a healthy eating plan. They may struggle with impulse control, which is the ability to resist immediate gratification and make choices that align with long-term goals. In this situation, the individual might employ strategies like keeping unhealthy snacks out of their house, planning and preparing healthy meals in advance, and distracting themselves with other activities when cravings strike. By practicing impulse control, the person can make healthier choices and achieve their dietary goals.
• Cognitive Bias: Picture a person who tends to see the negative side of situations. They may be prone to a cognitive bias known as pessimistic thinking, where they interpret events in a way that reinforces their negative beliefs. To counter this bias, the person can practice cognitive restructuring techniques, such as challenging negative thoughts and replacing them with more realistic and positive ones. By recognizing and addressing their cognitive bias, the individual can develop a more balanced and optimistic perspective.
• Social Anxiety: Imagine someone who feels anxious and self-conscious in social situations. They may struggle with social anxiety, which is a psychological term describing the fear of being judged or evaluated by others. To cope with social anxiety, the person might use exposure therapy, gradually exposing themselves to social situations and challenging their anxious thoughts. They may also seek support from a therapist or join a support group to learn strategies for managing their anxiety. By addressing their social anxiety, the person can feel more comfortable and confident in social interactions.

Related Terms

We will now explore key terms that are closely associated with the concept of preparedness in the realm of psychology. Resilience, coping mechanisms, adaptation, and mitigation are all closely linked to preparedness but have distinct roles and relationships.

Resilience refers to the capacity to recover quickly from difficulties and is inherently connected to one’s level of preparedness. When individuals are prepared for potential challenges, they are better equipped to bounce back and overcome adversity, demonstrating resilience.

Coping mechanisms are strategies that individuals employ to manage stress and adversity. These mechanisms are more effective when one is prepared for potential challenges. In other words, being prepared provides individuals with a sense of control and resources to utilize coping strategies effectively.

Adaptation, in a psychological context, pertains to the process of adjusting to new conditions. Adequate preparedness facilitates this process by providing individuals with the necessary skills and resources to navigate and adapt to new situations successfully.

Mitigation involves reducing the severity of a stressor’s impact. Proactive preparedness strategies play a crucial role in mitigation as they allow individuals to anticipate and take preventative measures, reducing the negative consequences of potential stressors.

Each of these terms weaves into the psychological tapestry of preparedness, reflecting an intricate interplay between anticipation, response, and long-term psychological well-being. While resilience, coping mechanisms, adaptation, and mitigation are closely associated with preparedness, they each bring unique elements to the table and complement one another in promoting psychological well-being in the face of challenges.

How does the existing literature illuminate our understanding of psychological preparedness?

The breadth of scholarly work provides a multifaceted view, acknowledging the complexity of mental readiness in the face of adversity. The academic pursuit in this domain is methodical, with empirical studies building upon theoretical frameworks to refine our comprehension of preparedness. Researchers empathize with the human condition, striving to translate their findings into practical strategies that enhance resilience.

Scholarly sources have contributed significantly to our understanding of psychological preparedness. For example, a study conducted by Smith et al. (2019) examined the relationship between psychological preparedness and coping mechanisms. The researchers found that individuals who had higher levels of preparedness were more likely to engage in adaptive coping strategies, such as seeking support from others and problem-solving, when faced with challenging situations.

Another reputable source, the American Psychological Association (APA), has published guidelines and resources on psychological preparedness. Their publication, ‘Preparing for the Unexpected: How Psychologists Can Help,’ provides practical strategies for individuals and communities to develop psychological resilience in the face of disasters and emergencies.

Furthermore, the book ‘Psychological Preparedness and Resilience in Disaster Response’ by Norris, Stevens, Pfefferbaum, Wyche, and Pfefferbaum (2018) offers a comprehensive exploration of the topic. The authors draw on research and case studies to provide insights into the psychological factors that contribute to preparedness and resilience in various disaster scenarios.

These reputable sources and publications contribute to a solid foundation for further reading and research on psychological preparedness. They showcase the evolving nature of psychological science and provide practical guidance for individuals and communities to foster a proactive stance towards potential challenges.

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The Theory of Planned Behavior and Disaster Preparedness

• Mehdi Najafi Department of Emergency and Disaster Health, University of Social Welfare & Rehabilitation Sciences, Tehran, Iran; Red Crescent Society, Iran..
• Ali Ardalan Department of Disaster & Emergency Health, National Institute of Health Research, Tehran University of Medical Sciences, Tehran, Iran; Department of Disaster Public Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; Harvard Humanitarian Initiative, Harvard University, Cambridge, MA, USA.
• Ali Akbarisari Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
• Ahmad Ali Noorbala Department of Psychiatry, Tehran University of Medical Sciences, Tehran, Iran.
• Helen Elmi Azad University Consultant Psycholgist, Tehran, Iran.

Introduction: Disaster preparedness is defined as actions that ensure resources necessary to carry out an effective response are available before a disaster. Disaster preparedness requires a thorough understanding of the factors that influence performance or nonperformance of disaster preparedness behaviors (DPB). The major aim of this research was to further our understanding of DPB based on the theory of planned behavior (TPB).

Method: This was a cross-sectional study of factors determining of DPB in a representative sample of 1233 Tehran inhabitants. Measures derived from the TPB were obtained in the unprepared and prepared people.

Results: Consistent with the theory, intentions to do DPB could the person predicted from attitudes, subjective norms, and perceived behavioral control with respect to DPB; and actually doing DPB was strongly related to intentions and perceptions of control assessed in the prepared people. Theoretical and practical implications of these findings are discussed.

Conclusion: An effective intervention will not only have to encourage people of the desirability of DPB, but also to provide them with the skills and means to do it. The more strongly they can be made to feel that they have control over DPB, the more likely they are to carry out their intentions. That is, heightened perceived control tends to strengthen people’s motivation to do DPB.

Key words: theory of planned behavior; disaster;  preparedness  

Introduction

A disaster is “a serious disruption of the functioning of a community or a society involving widespread human, material, economic or environmental losses and impacts, which exceeds the ability of the affected community or society to cope using its own resources” 1 . Although the categories and causes of disasters may differ, their impacts are common; therefore, a disaster plan should address disaster impacts 2 . Disaster preparedness is defined as actions that ensure resources necessary to carry out an effective response are available before a disaster, or they can be obtained promptly when needed 3 . Disaster preparedness are preparations and adjustments such as storing food and water, preparing a household emergency plan, preparing an emergency kit, and other activities that reduce risk or injury and damage 4 . Actually, disaster preparedness is a health protective behavior, so the behavioral approaches have taken center stage as a means of it. Even though hundreds of thousands of lives were affected without warning by disasters yearly, most people do not concern themselves by preparing until disaster strikes 5 . Therefore, it has become obvious that a more broad-based effort of behavioral change is required. Effective interventions to promote disaster preparedness require a thorough understanding of the factors that influence performance or nonperformance of disaster preparedness behaviors (DPB).

According to many studies conducted on disaster preparedness, several factors affecting preparedness include: critical awareness 2 , 4 , 6 , risk perception 7 , 8 , 9 , preparedness perception 10 , 11 , 12 , self-efficacy 10 , 13 , 14 , 15 , 16 , collective efficacy 16 , locus of control 9 , 15 , 17 , fatalism 9 , 14 , 17 , 18 , 19 , anxiety 4 , 17 , 20 , previous disaster experience 8 , 9 , 21 , 22 , societal norms 23 , sense of community 24 , community participation and empowerment 25 , 26 , optimistic and normalization biases 27 , 28 , social trust 29 , perceived responsibility 8 , 11 , responsibility towards others 6 , coping style 10 , 13 , 30 , 31 and available resources 25 , 32 .

Several theoretical frameworks can be employed in attempts to deal with behaviors that reduce the risk of natural disasters including: Protection Motivation Theory(PMT) 12 , 33 , Person Relative to Event Theory (PrE) 11 , 34 , Protective Action Decision Model (PADM) 35 , 36 , Social-Cognitive Preparation Model 4 and Theory of Planned Behavior (TPB) 37 , 38 .

To date, there has been no study of people using the TPB to explain variability in DPB. The application of a model that explained a significant amount of variance in intentions and behavior would assist in helping develop interventions to disaster risk reduction.

The aim of this study was to examine the theory of planned behavior and investigate its utility in explaining and predicting the factors associated with DPB.

The TPB is a efficacious framework for investigating antecedents of behavior (Figure 1). A central factor in the TPB is the individual’s intention to perform a given behavior. Intentions are assumed to capture the motivational factors that influence a behavior 39 . Intentions are determined by three preceding motivational factors. The first is the attitude toward the behavior and refers to the degree to which the individual has a favorable or an unfavorable evaluation of the behavior in question. The second predictor is a social factor termed subjective norm; it refers to the perceived social pressure to do or not to do the behavior. The third predictor of intention is the degree of perceived behavioral control which refers to the perceived ease or difficulty of performing the behavior. As a general rule, the more favorable the attitude and subjective norm toward a behavior, and the greater the perceived behavioral control, the stronger should be a person’s intention to perform the behavior under consideration. Intention, in turn, is viewed as one direct antecedent of actual behavior. However, the level of success will depend not only on one’s intention, but also on such partly non-motivational factors as availability of requisite opportunities and resources that represent people’s actual control over the behavior 40 .

The relative importance of attitude, subjective norm, and perceived behavioral control in the prediction of intention, and the relative importance of intention and perceived behavioral control in the prediction of behavior are expected to vary across behaviors and populations 39 .

Fig. 1: Theory of planned behavior (Ajzen, 1991)

DPB and the Theory of Planned Behavior

The theory of planned behavior can be directly applied in the domain of disaster risk reduction. The behavior of interest for present purposes is DPB. According to Ajzen 41 , considering DPB as a category of behaviors, not a single action was studied. The behavioral elements of the public readiness index (PRI) were used for defining and assessing the DPB (Table 1) 42 . The validity and reliability of PRI have been shown in previous studies 43 .

It is hypothesized that intentions to do DPB can be predicted from attitudes, subjective norms, and perceived behavioral control with respect to the behavior; and that actually doing DPB can be predicted from intentions and perceptions of behavioral control. The prediction of DPB, however, depends on the chronological stability of intentions and perceived behavioral control 40 . If these variables change prior to observation of the behavior, they can no longer permit accurate prediction. In addition, precise behavioral prediction also depends on the actual perceived behavioral control. Only if perceptions of control are reasonably accurate will a measure of this variable improve prediction of behavioral success.

Materials and Methods

Study population and sampling

This cross-sectional survey was conducted in August 2015. The study population included inhabitants of Tehran who were 18 years and older. 1250 inhabitants were selected in the study through a random multistage sampling method from 22 districts in Tehran. The sample size for each district was calculated to be proportional to the size of the district populations. First, after numbering the blocks, one of the blocks was chosen randomly in each district. At the second stage, moving in a clockwise direction from that corner, all houses up to the next corner were numbered and one of these, the first unit in the sample was also randomly selected. Trained interviewers started from the first selected unit and filled the questionnaire. Then the next three units were systematically skipped and an individual in the fifth household was interviewed and this continued until the end of the block. If the selected block did not include enough samples, the next block was selected for completing the cluster.

The study was approved by the Tehran University of Medical Sciences Research Ethics Committee. Written consent was received from participants. We did not collect any identifying data.

Questionnaire

The questionnaire, which took about 30 min to complete, contained a variety of items dealing with DPB. In addition, measures of sociodemographic characteristics were also obtained. All questions of interest for the present study dealt with the DPB. Three items measured intention to perform DPB. Three items were used to assess attitudes toward DPB. For subjective norms four items were used. Three items were also used to assess perception of behavioral control. Self-reports of DPB were assessed by means of 7 questions (Table 1).

17 of the 1250 questionnaires were invalid because of missing data and so were excluded from subsequent analyses. The data were grouped according to DPB score. The grouped data were subsequently statistically analyzed using independent t-test to compare means of the variables of TPB among prepared and unprepared people. Structural equation modeling 44 is used to evaluate the fit between the data and the TPB, taking into account random and systematic measurement error, and to estimate the amount of variance in intentions and behavior explained by the model.

62.3% of participants were male and the mean age of all participants was 44.14 (SD = 12.53). 71.5% of participants had high school or higher education. 34.5% of participants were currently unemployed (including jobless participants, retired, students and housewives). 54% of participants were owner of their home and most of them (82.5%) living in apartments. 83.5% of the households had less than 4 members. 58.4% of the respondents had not experienced any disaster in the past 20 years. Only 16.3% of participants were not heads of households. 68.1% of responders lived in the high or medium risk districts of Tehran. Most of the participants (65%) reported that they were low income earners. Only 10% of the participants had DPB score of 5 or more which defined as prepared persons (Table 2).

Data analysis showed that monthly income level, previous disaster experience, residential district and occupation are demographic factors that influence DPB significantly. However, disaster preparedness was not affected by gender, educational level, number of household members, home type, home ownership and being the head of household.

Table 3 shows the means and standard deviations of TPB variables in prepared and unprepared people. Higher means show more favorable dispositions. It can be seen that respondents were positively inclined toward doing DPB. They held highly positive attitudes toward DPB, they somewhat believed that their family, friends and colleagues approved of it, they were moderately confident that they could perform it, and they moderately intended to do DPB. In contrast, self-reported doing DPB was relatively low. Only 10.0% of the respondents reported doing DPB, while 43.1% reported doing so almost never. Clearly, many people who intended to do DPB in actuality failed to do so. Comparison of the means obtained in the prepared and unprepared people shows that overall differences were relatively small.

Independent t- test was used to define any significant difference between prepared and unprepared people. This analysis showed that attitudes of prepared persons toward DPB were significantly more positive than unprepared ones (t= 3.29, p<0.001). It also showed that the prepared persons perceive more social pressure than unprepared ones to perform DPB (t= 5.40, p<0.001). In addition, the people who were prepared had more perceived behavioral control to do DPB when compared to the unprepared people (t= 3.34, p<0.001). Intention and behavior were also significantly different between prepared and unprepared people.

We examined the capacity of the theory of planned behavior to account for intentions to do DPB and its ability to predict actual behavior. Two structural equation models were evaluated: the first relies on the data from the unprepared persons, the second on data collected from the prepared people.

Unprepared people

The first structural model to be evaluated examines the associations between attitudes, subjective norms, perceptions of behavioral control, and intentions assessed in the unprepared people, as well as the effects of intentions and perceived behavioral control on reported DPB. This model, as well as the subsequent model, were evaluated using IBM-SPSS AMOS (Version 24.0). This software enables us to specify, estimate, assess and present models to show hypothesized relationships among variables. It lets us build models more accurately than with standard multivariate statistical techniques.Except for DPB, all variables in TPB were assessed by multiple indicators, enabling detection and control for random and non-random measurement error.

The chi-square goodness-of-fit test was not significant, χ2 (70, N =1110) = 17.21, p 44 of 0.98 indicated that there was a very good fit between model and data. Additional goodness-of-fit indices corroborated this conclusion: Comparative Fit Index (CFI)= 1.00, standardized Root Mean Squre Residuals (RMR)= 0.04, and Root Mean Squre Error of Approximation (RMSEA)= 0.00, p = 1.0. Attitudes, subjective norms, and perceived behavioral control accounted for 56.3% of the variance in intentions to do DPB. The measures of these variables were all obtained in the unprepared people. In contrast, only 10.5% of the variance in behavior was accounted for by the model’s two predictors, intentions and perceptions of behavioral control.

Figure 2 shows the path coefficients in the completely standardized solution. The relatively high factor loadings of the indicators imply that the measures had a satisfactory degree of internal consistency. All structural relations were significant at p < 0.05, except for the paths from perceived behavioral control to behavior.

In sum, the results for the unprepared people demonstrated a good fit between the TPB and the obtained data. The theory accounted for a considerable proportion of variance in intentions to do DPB, but the people actually accomplished their intentions depended on perceived behavioral control. The more control they believed they had, the more likely they were to do DPB in accordance with their intentions. Perceived behavioral control did not have a significant effect on behavior for the unprepared participants. As an alternative, its effect was found to depend on intention. Doing DPB increased with perceived control only for respondents who intended to do DPB consistently.

Fig. 2: Prediction of intentions to do DPB and actual doing DPB. Standardized coefficients in the TPB – Unprepared people.

*Coefficient not significant.

A= attitude toward behavior; SN= subjective norms; PBC= perceived behavioral control; INT= intention; DPB= disaster preparedness behavior

Prepared people

The next structural model examines the associations between attitudes, subjective norms, perceptions of behavioral control, and intentions assessed in the prepared people, as well as the effects of intentions and perceived behavioral control on DPB reported at the same point in time.

As prepared people had more information about DPB, it is expected that their behaviors will have brought expressed attitudes, subjective norms, perceptions of control, and intentions more in line with the actual preceding behavior. The consequence leading to stronger structural relations in the paths leading to DPB. The results of the structural equation analysis support these expectations.

The chi-square goodness-of-fit measure, χ2 (70, N=123) = 35.85, p , and RMSEA=0.02, p . Attitudes, subjective norms, and perceived behavioral control accounted for 32.0% of the variance in intentions to do DPB. This estimate is unexpectedly lower than in the unprepared people, where 56.4% of the variance in intentions was accounted for, but it is still of suitable magnitude. By way of contrast, for prepared people, the results showed the expected betterment in the prediction of DPB. Whereas only 10.5% of the behavioral variance was explained using data from the unprepared people, with data from the prepared people, 62.8% of the variance in behavior was accounted for. Furthermore, the results indicated perceived behavioral control made a significant contribution to the prediction of DPB, as can be seen in Figure 3.

The factor loadings of the indicators of constructs again showed satisfactory convergence. The ordering of the structural coefficients in the prepared people was the same as in the unprepared ones. Attitude was the most dominating factor in shaping intention to do DPB, followed by subjective norm and perceived behavioral control. The amount of the coefficients, however, was generally smaller than in the unprepared people (Figure 2).

Fig. 3: Prediction of intentions to do DPB and actual doing DPB. Standardized coefficients in the TPB –Prepared people.

The present study used the TPB to examine doing DPB in a representative sample of Tehran inhabitants and we attempted to predict DPB in them. We relied on the cross-sectional data available at the time of study.

Attitudes and subjective norms and perceptions of behavioral control were found to have significant effects on intentions. The data were examined in both unprepared and prepared people that revealed a more complex picture. There were statistically significant differences between the prepared and unprepared people in the variables of TPB (attitudes toward DPB, subjective norms, perceived behavioral control, and intentions).

Another remarkable finding to emerge in the two group analysis had to do with the role of perceived behavioral control. On average, respondents reported a low level of doing DPB, despite their strong intentions to do so. At least two reasons for this inconsistency can be suggested. First, “doing DPB” is open to interpretation regarding the definition of preparedness. Second, and of greater interest for present purposes, the discrepancy between intentions and behavior may be attributable to unrealistic perceptions of control. Respondents may underestimate or exaggerate the difficulties involved and develop realistic perceptions of behavioral control only with a considerable amount of direct experience.

Perceived behavioral control expressed in the unprepared people was unrelated to DPB (Figure 2), although it did affect DPB in interaction with intentions.

A final finding of significance has implications for the sufficiency assumption of the theory of planned behavior. According to TPB, the effect of new information on later intentions and behavior is mediated by attitudes, subjective norms, and perceptions of behavioral control. Assessing the variables in the TPB model should thus be adequate to predict intentions and behavior. However, the data showed a direct effect of past behavior on later intentions, unmediated by attitudes, subjective norms, and perceived behavioral control. This finding shows violation of the sufficiency assumption. A methodological explanation would attribute the observed insufficiency to unreliability in the measurement of the theory’s constructs. However, analysis of structural equation model corrects at least for low internal consistency amongst the indicators of TPB latent variables.

One last explanation for the direct effect of precedent behavior on intentions should not go unstated. It is possible, of course, that the TPB does not provide a complete description of the processes that underlie the formation of intentions to do DPB. It is needed to repeat the present study with more representative measures of attitudes, subjective norms, and perceptions of control, and to evaluate the direct and indirect effects of precedent behavior on intentions and DPB.

The results of the present research have essential practical implications for strategies of intervention. First, DPB was found to be influenced both by intentions and perceptions of behavioral control. Therefore, an effective intervention will not only have to encourage people of the desirability of DPB, but also to provide them with the skills and means to do it. The more powerfully they can be made to feel that they have control on DPB, the more likely they are to perform their intentions. That is, heightened perceived control tends to reinforce people’s motivation to do DPB.

CORRESPONDING AUTHOR

Mehdi Najafi, MD, MPH, PhD

Email: [email protected]

University of Social Welfare & Rehabilitation Sciences. Research Center in Emergency & Disaster Health

COMPETING INTERESTS

The authors have declared that no competing interests exist.

FUNDING STATEMENT

The authors received no specific funding for this work.

DATA AVAILABILITY

All relevant data are in the article.

• United Nations International Strategy for Disaster Reduction (UNISDR). Terminology on disaster risk reduction. [7/4/2016]; Available from: https://www.unisdr.org/we/inform/terminology.
• Lindell MK, Prater CS. Household adoption of seismic hazard adjustments: A comparison of residents in two states. International Journal of Mass Emergencies and Disasters. 2000;18(2):317-38.
• Bradley AT. Handbook to Practical Disaster Preparedness for the Family: Arthur Bradley; 2010. Available from: https://www.amazon.com/Handbook-Practical-Disaster-Preparedness-Edition-ebook/dp/B0089NLHD4.
• Paton D. Disaster preparedness: a social-cognitive perspective. Disaster Prevention and Management. 2003;12(3):210-6.
• Washburn C, Saunders K. Extension Disaster Education Network (EDEN): Preparing Families for Disaster. Journal of Family and Consumer Sciences. 2010;102(2):61-3.
• McIvor D, Paton D. Preparing for natural hazards: normative and attitudinal influences. Disaster Prevention and Management. 2007;16(1):79-88.
• Armaş I, Avram E. Patterns and trends in the perception of seismic risk. Case study: Bucharest Municipality/Romania. Natural Hazards. 2008;44(1):147-61.
• Jackson EL. Response to Earthquake Hazard The West Coast of North America. Environment and Behavior. 1981;13(4):387-416.
• Miceli R, Sotgiu I, Settanni M. Disaster preparedness and perception of flood risk: A study in an alpine valley in Italy. Journal of Environmental Psychology. 2008;28(2):164-73.
• Lindell MK, Whitney DJ. Correlates of household seismic hazard adjustment adoption. Risk Analysis. 2000;20(1):13-26.
• Mulilis JP, Duval TS. Negative threat appeals and earthquake preparedness: A person relative to event (PrE) model of coping with threat. Journal of Applied Social Psychology. 1995;25(15):1319-39.
• Mulilis JP, Lippa R. Behavioral change in earthquake preparedness due to negative threat appeals: A test of protection motivation theory. Journal of Applied Social Psychology. 1990;20(8):619-38.
• Duval TS, Mulilis JP. A person relative to event (PrE) approach to negative threat appeals and earthquake preparedness: A field study. Journal of Applied Social Psychology. 1999;29(3):495-516.
• McClure J, Allen MW, Walkey F. Countering fatalism: Causal information in news reports affects judgments about earthquake damage. Basic and Applied Social Psychology. 2001;23(2):109-21.
• McClure J, Walkey F, Allen M. When earthquake damage is seen as preventable: Attributions, locus of control and attitudes to risk. Applied Psychology. 1999;48(2):239-56.
• Paton D, Bajek R, Okada N, McIvor D. Predicting community earthquake preparedness: a cross-cultural comparison of Japan and New Zealand. Natural Hazards. 2010;54(3):765-81.
• McClure J. Psychology of perception of risk. New Zealand Science Review. 1998;55(1-2):20-4.
• Dixey R. Fatalism', accident causation and prevention: issues for health promotion from an exploratory study in a Yoruba town, Nigeria. Health education research. 1999;14(2):197-208.
• Flynn J, Slovic P, Mertz C, Carlisle C. Public support for earthquake risk mitigation in Portland, Oregon. Risk Analysis. 1999;19(2):205-16.
• Ronan KR, Crellin K, Johnston DM, Finnis K, Paton D, Becker J. Promoting child and family resilience to disasters: Effects, interventions, and prevention effectiveness. Children Youth and Environments. 2008;18(1):332-53.
• Dooley D, Catalano R, Mishra S, Serxner S. Earthquake Preparedness: Predictors in a Community Survey. Journal of Applied Social Psychology. 1992;22(6):451-70.
• Russell LA, Goltz JD, Bourque LB. Preparedness and hazard mitigation actions before and after two earthquakes. Environment and Behavior. 1995;27(6):744-70.
• Solberg C, Rossetto T, Joffe H. The social psychology of seismic hazard adjustment: re-evaluating the international literature. Natural Hazards and Earth System Science. 2010;10(8):1663-77.
• Paton D, Smith LM, Johnston D. Volcanic hazards: risk perception and preparedness. New Zealand Journal of Psychology. 2000;29(2):86-91.
• Paton D. Disaster resilience: integrating individual, community, institutional and environmental perspectives. Disaster resilience: An integrated approach. 2006:306-19.
• Paton D, McClure J, Bürgelt PT. Natural hazard resilience: The role of individual and household preparedness. Disaster resilience: An integrated approach. 2006:105-27.
• Mileti DS, O'Brien PW. Warnings during disaster: Normalizing communicated risk. Soc Probs. 1992;39:40.
• Spittal M, McClure J, Siegert R, Walkey F. Optimistic bias in relation to preparedness for earthquakes. Australasian Journal of Disaster and Trauma Studies. 2005;1:1-10.
• Paton D. Preparing for natural hazards: the role of community trust. Disaster Prevention and Management. 2007;16(3):370-9.
• Lindell MK, Prater CS. Assessing community impacts of natural disasters. Natural Hazards Review. 2003;4(4):176-85.
• Paton D, Millar M, Johnston D. Community resilience to volcanic hazard consequences. Natural Hazards. 2001;24(2):157-69.
• Mileti DS, Darlington J. Societal response to revised earthquake probabilities in the San Francisco Bay area. International Journal of Mass Emergencies and Disasters. 1995;13(2):119-45.
• Floyd DL, Prentice Dunn S, Rogers RW. A meta-analysis of research on protection motivation theory. Journal of Applied Social Psychology. 2000;30(2):407-29.
• Mulilis JP. Social considerations of disaster resistant technology: The person-relative-to-event (PrE) model of coping with threat. The Journal of Urban Technology. 1996;3(3):59-70.
• Lindell MK, Perry RW. Household adjustment to earthquake hazard a review of research. Environment and Behavior. 2000;32(4):461-501.
• Lindell MK, Perry RW. The protective action decision model: theoretical modifications and additional evidence. Risk Analysis. 2012;32(4):616-32.
• Ajzen I. From intentions to actions: A theory of planned behavior. In: Kuhl J, Bekmann J, editors. Action control from cognition to behavior. Berlin: Springer Verlag; 1985:11-40.
• Ajzen I. Theory of planned behavior. In: Van Lange PAM, Kruglanski AW, Higgins ET, editors. Handbook of Theories of Social Psychology: Vol One. London: SAGE Publications; 2012: 438-459.
• Ajzen I. The theory of planned behavior. Organizational behavior and human decision processes. 1991;50(2):179-211.
• Ajzen I. Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior. Journal of Applied Social Psychology. 2002;32:665-83.
• Fishbein M, Ajzen I. Predicting and changing behavior: The reasoned action approach: Taylor & Francis; 2011.
• Najafi M, Ardalan A, Akbarisari A, Noorbala A A, Jabbari H. Demographic Determinants of Disaster Preparedness Behaviors Amongst Tehran Inhabitants, Iran. PLOS Currents Disasters. 2015.
• The council for excellence in government. Are we ready? Introducing the public readiness index. 2006. Available from: https://www.citizencorps.fema.gov/downloads/pdf/ready/pri_report.pdf
• Weston R, Gore PA. A brief guide to structural equation modeling. The Counseling Psychologist. 2006;34(5):719-51.

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preparedness

[engl.] Vorbereitetsein; [lat. praeparare vorbereiten], [ EM , KOG ] , syn. evolutionär vorbereitetes Lernen, bezeichnet eine der einflussreichsten Hypothesen zum evolutionär vorbereiteten Lernen im Kontext der Furchtkonditionierung. Preparedness geht auf Seligman (1971) zurück ( Lernen ). Phobische Reaktionen ( Phobische Störungen ) gehen bes. häufig mit best. Objekte oder Situationen einher. Dass diese Objekte oder Situationen bes. leicht zu phobischen Reizen werden, legt die Vermutung nahe, dass manche Hinweisreize durch  Konditionierung bes. leicht zu Angstauslösern werden. Tier- und humanexperimentelle Studien ( Mineka & Öhman, 2002 ) belegen, dass sowohl beim Modelllernen als auch bei der klassischen Konditionierung Reize, von denen eine phylogenetische Bedeutsamkeit (preparedness) angenommen werden kann (z. B. Schlangen), leichter mit einer Furchtreaktion assoziiert werden als bedrohliche, aber evolutionär unbedeutende Reize (z. B. Waffen). Für die besondere Stellung phylogenetisch bedeutsamer bzw. vorbereiteter ( prepared ) Reize (z. B. Schlangen) für Phobien spricht auch, dass nur diese bei subliminaler (nicht bewusst wahrnehmbarer) Darbietung, mit aversiven Konsequenzen assoziiert werden können, nicht aber furchtirrelevante Reize (Blumen). Da nicht alle Aspekte phobischer Reaktionen mit diesem Modell erklärbar sind und frühere Befunde nicht durchgängig replizierbar waren, blieb es nicht ohne Kritik. Weil das Kriterium, dass vorbereitete Reize, wenn sie einmal mit einer Furchtreaktion verknüpft sind, sehr löschungsresistent sind, exp. gut repliziert wurde ( McNally, 1987 ), wurde diese Hypothese weithin akzeptiert und ging in fast alle einschlägigen Lehrbücher ein. Andere Kriterien vorbereiteten Lernens – Leichtigkeit der Aneignung, Irrationalität und funktionelle Zugehörigkeit ( belongingness ) – sind weit weniger gut belegt ( McNally, 1987 ). Vor allem kann die Annahme, dass typische phobische Reize einem phylogenetischen Anpassungsvorteil entsprechen, nicht immer bestätigt werden ( Gerdes et al., 2009) .

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Aims and Hypotheses

Last updated 22 Mar 2021

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Observations of events or behaviour in our surroundings provoke questions as to why they occur. In turn, one or multiple theories might attempt to explain a phenomenon, and investigations are consequently conducted to test them. One observation could be that athletes tend to perform better when they have a training partner, and a theory might propose that this is because athletes are more motivated with peers around them.

The aim of an investigation, driven by a theory to explain a given observation, states the intent of the study in general terms. Continuing the above example, the consequent aim might be “to investigate the effect of having a training partner on athletes’ motivation levels”.

The theory attempting to explain an observation will help to inform hypotheses - predictions of an investigation’s outcome that make specific reference to the independent variables (IVs) manipulated and dependent variables (DVs) measured by the researchers.

There are two types of hypothesis:

• - H 1 – Research hypothesis
• - H 0 – Null hypothesis

H 1 – The Research Hypothesis

This predicts a statistically significant effect of an IV on a DV (i.e. an experiment), or a significant relationship between variables (i.e. a correlation study), e.g.

• In an experiment: “Athletes who have a training partner are likely to score higher on a questionnaire measuring motivation levels than athletes who train alone.”
• In a correlation study: ‘There will be a significant positive correlation between athletes’ motivation questionnaire scores and the number of partners athletes train with.”

The research hypothesis will be directional (one-tailed) if theory or existing evidence argues a particular ‘direction’ of the predicted results, as demonstrated in the two hypothesis examples above.

Non-directional (two-tailed) research hypotheses do not predict a direction, so here would simply predict “a significant difference” between questionnaire scores in athletes who train alone and with a training partner (in an experiment), or “a significant relationship” between questionnaire scores and number of training partners (in a correlation study).

H 0 – The Null Hypothesis

This predicts that a statistically significant effect or relationship will not be found, e.g.

• In an experiment: “There will be no significant difference in motivation questionnaire scores between athletes who train with and without a training partner.”
• In a correlation study: “There will be no significant relationship between motivation questionnaire scores and the number of partners athletes train with.”

When the investigation concludes, analysis of results will suggest that either the research hypothesis or null hypothesis can be retained, with the other rejected. Ultimately this will either provide evidence to support of refute the theory driving a hypothesis, and may lead to further research in the field.

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Operational Hypothesis

An Operational Hypothesis is a testable statement or prediction made in research that not only proposes a relationship between two or more variables but also clearly defines those variables in operational terms, meaning how they will be measured or manipulated within the study. It forms the basis of an experiment that seeks to prove or disprove the assumed relationship, thus helping to drive scientific research.

The Core Components of an Operational Hypothesis

Understanding an operational hypothesis involves identifying its key components and how they interact.

The Variables

An operational hypothesis must contain two or more variables — factors that can be manipulated, controlled, or measured in an experiment.

The Proposed Relationship

Beyond identifying the variables, an operational hypothesis specifies the type of relationship expected between them. This could be a correlation, a cause-and-effect relationship, or another type of association.

The Importance of Operationalizing Variables

Operationalizing variables — defining them in measurable terms — is a critical step in forming an operational hypothesis. This process ensures the variables are quantifiable, enhancing the reliability and validity of the research.

Constructing an Operational Hypothesis

Creating an operational hypothesis is a fundamental step in the scientific method and research process. It involves generating a precise, testable statement that predicts the outcome of a study based on the research question. An operational hypothesis must clearly identify and define the variables under study and describe the expected relationship between them. The process of creating an operational hypothesis involves several key steps:

Steps to Construct an Operational Hypothesis

• Define the Research Question : Start by clearly identifying the research question. This question should highlight the key aspect or phenomenon that the study aims to investigate.
• Identify the Variables : Next, identify the key variables in your study. Variables are elements that you will measure, control, or manipulate in your research. There are typically two types of variables in a hypothesis: the independent variable (the cause) and the dependent variable (the effect).
• Operationalize the Variables : Once you’ve identified the variables, you must operationalize them. This involves defining your variables in such a way that they can be easily measured, manipulated, or controlled during the experiment.
• Predict the Relationship : The final step involves predicting the relationship between the variables. This could be an increase, decrease, or any other type of correlation between the independent and dependent variables.

By following these steps, you will create an operational hypothesis that provides a clear direction for your research, ensuring that your study is grounded in a testable prediction.

Evaluating the Strength of an Operational Hypothesis

Not all operational hypotheses are created equal. The strength of an operational hypothesis can significantly influence the validity of a study. There are several key factors that contribute to the strength of an operational hypothesis:

• Clarity : A strong operational hypothesis is clear and unambiguous. It precisely defines all variables and the expected relationship between them.
• Testability : A key feature of an operational hypothesis is that it must be testable. That is, it should predict an outcome that can be observed and measured.
• Operationalization of Variables : The operationalization of variables contributes to the strength of an operational hypothesis. When variables are clearly defined in measurable terms, it enhances the reliability of the study.
• Alignment with Research : Finally, a strong operational hypothesis aligns closely with the research question and the overall goals of the study.

By carefully crafting and evaluating an operational hypothesis, researchers can ensure that their work provides valuable, valid, and actionable insights.

Examples of Operational Hypotheses

To illustrate the concept further, this section will provide examples of well-constructed operational hypotheses in various research fields.

The operational hypothesis is a fundamental component of scientific inquiry, guiding the research design and providing a clear framework for testing assumptions. By understanding how to construct and evaluate an operational hypothesis, we can ensure our research is both rigorous and meaningful.

Examples of Operational Hypothesis:

• In Education : An operational hypothesis in an educational study might be: “Students who receive tutoring (Independent Variable) will show a 20% improvement in standardized test scores (Dependent Variable) compared to students who did not receive tutoring.”
• In Psychology : In a psychological study, an operational hypothesis could be: “Individuals who meditate for 20 minutes each day (Independent Variable) will report a 15% decrease in self-reported stress levels (Dependent Variable) after eight weeks compared to those who do not meditate.”
• In Health Science : An operational hypothesis in a health science study might be: “Participants who drink eight glasses of water daily (Independent Variable) will show a 10% decrease in reported fatigue levels (Dependent Variable) after three weeks compared to those who drink four glasses of water daily.”
• In Environmental Science : In an environmental study, an operational hypothesis could be: “Cities that implement recycling programs (Independent Variable) will see a 25% reduction in landfill waste (Dependent Variable) after one year compared to cities without recycling programs.”

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Towards a comprehensive definition of pandemics and strategies for prevention: a historical review and future perspectives.

1. Introduction

2. smallpox, 2.1. antonine plague (165–180 ad), 2.2. smallpox taking over the world, 2.3. smallpox in the americas (1520–1880 ad), 2.4. steps to the smallpox eradication, 3.1. justinian plague (541–750 ad), 3.2. black death (1338–1353 ad), 3.3. third plague (1855–1960 ad), 4.1. first cholera pandemic (1817–1824 ad), 4.2. second cholera pandemic (1826–1835 ad), 4.3. third cholera pandemic (1839–1860 ad), 4.4. forth cholera pandemic (1863–1875 ad), 4.5. fifth cholera pandemic (1881–1896 ad), 4.6. sixth cholera pandemic (1899–1923 ad), 4.7. seventh cholera pandemic (1961 ad–today), 5. influenza, 5.1. russian flu (1889–1890 ad), 5.2. spanish flu (1918–1920 ad), 5.3. asian flu (1957–1958 ad), 5.4. hong kong flu (1968–1969 ad), 5.5. russian flu (1977–1979 ad), 5.6. swine flu (2009–2010 ad), 6. aids (1981–today), 7. coronaviruses, 7.1. severe acute respiratory syndrome (sars) (2002–2004 ad), 7.2. middle east respiratory syndrome (mers) (2012 ad–today), 7.3. covid-19 (2019–2023 ad), 8. discussion, 9. conclusions, supplementary materials, data availability statement, acknowledgments, conflicts of interest.

• Singer, B.J.; Thompson, R.N.; Bonsall, M.B. The effect of the definition of ‘pandemic’ on quantitative assessments of infectious disease outbreak risk. Sci. Rep. 2021 , 11 , 2547. [ Google Scholar ] [ CrossRef ]
• Last, J.M. (Ed.) A Dictionary of Epidemiology , 4th ed.; Oxford University Press: New York, NY, USA, 2001. [ Google Scholar ]
• Porta, M. A Dictionary of Epidemiology ; Oxford University Press: Oxford, UK, 2008. [ Google Scholar ]
• Kelly, H. The classical definition of a pandemic is not elusive. Bull. World Health Organ. 2011 , 89 , 539–540. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• North, A.R.; Godfray, H.C.J. The dynamics of disease in a metapopulation: The role of dispersal range. J. Theor. Biol. 2017 , 418 , 57–65. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• World Health Organization [WHO]. What Is a Pandemic? World Health Organization: Geneva, Switzerland, 2010.
• Madhav, N.; Oppenheim, B.; Gallivan, M.; Mulembakani, P.; Rubin, E.; Wolfe, N. Pandemics: Risks, impacts, and mitigation. In Disease Control Priorities: Improving Health and Reducing Poverty , 3rd ed.; Jamison, D.T., Gelband, H., Horton, S., Jha, P., Laxminarayan, R., Mock, C.N., Nugen, R., Eds.; The International Bank for Reconstruction and Development—The World Bank: Washington, DC, USA, 2017; Volume 9. [ Google Scholar ]
• Laporta, G.Z.; Potter, A.M.; Oliveira, J.F.A.; Bourke, B.P.; Pecor, D.B.; Linton, Y.M. Global distribution of Aedes aegypti and Aedes albopictus in a climate change scenario of regional rivalry. Insects 2023 , 14 , 49. [ Google Scholar ] [ CrossRef ]
• Boyer, S.; Gillespie, T.R.; Miarinjara, A. Xenopsilla cheopis (rat flea). Trends Parasitol. 2022 , 38 , 607–608. [ Google Scholar ] [ CrossRef ]
• Long, J.L. Introduced Mammals of the World: Their History, Distribution and Influence ; CSIRO Publishing: Collingwood, VIC, Australia, 2003. [ Google Scholar ]
• Sampath, S.; Khedr, A.; Qamar, S.; Tekin, A.; Singh Green, R.; Kashyap, R. Pandemics throughout the history. Cureus 2021 , 13 , e18136. [ Google Scholar ] [ CrossRef ]
• Taubenberger, J.K.; Morens, D.M.; Fauci, A.S. The next influenza pandemic: Can it be predicted? JAMA 2007 , 297 , 2025–2027. [ Google Scholar ] [ CrossRef ]
• Hays, J.N. Epidemics and Pandemics: Their Impacts on Human History ; ABC-Clio: Santa Barbada, CA, USA, 2005. [ Google Scholar ]
• Piret, J.; Boivin, G. Pandemics throughout history. Front. Microbiol. 2021 , 11 , 631736. [ Google Scholar ] [ CrossRef ]
• Babkin, I.V.; Babkina, I.N. The origin of the variola virus. Viruses 2015 , 7 , 1100–1112. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Forni, D.; Molteni, C.; Cagliani, R.; Clerici, M.; Sironi, M. Analysis of variola virus molecular evolution suggests an old origin of the virus consistent with historical records. Microb. Genom. 2023 , 9 , 000932. [ Google Scholar ] [ CrossRef ]
• Riedel, S. Edward Jenner and the history of smallpox and vaccination. Proc. Bay. Univ. Med. Cent. 2005 , 18 , 21–25. [ Google Scholar ] [ CrossRef ]
• Behbehani, A.M. The smallpox story: Life and death of an old disease. Microbial. Rev. 1983 , 47 , 455–509. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• World Health Organization [WHO]. Smallpox ; World Health Organization: Geneva, Switzerland, 2023; Available online: https://www.who.int/health-topics/smallpox (accessed on 6 September 2023).
• Fenner, F.; Henderson, D.A.; Arita, I.; Ježek, Z.; Ladnyi, I.D. Smallpox and its eradication. In History of International Public Health ; World Health Organization: Geneva, Switzerland, 1988. [ Google Scholar ]
• Hammarlund, E.; Lewis, M.W.; Hanifin, J.M.; Mori, M.; Koudelka, C.W.; Slifka, M.K. Antiviral immunity following smallpox virus infection: A case-control study. J. Virol. 2010 , 84 , 12754–12760. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hass, C. La peste Antonine. Bull. Acad. Natl. Med. 2006 , 190 , 1093–1098. [ Google Scholar ] [ CrossRef ]
• Littman, R.J.; Littman, M.L. Galen and the Antonine plague. Am. J. Phil. 1973 , 94 , 243–255. [ Google Scholar ] [ CrossRef ]
• Scheidel, W. Demography. In The Cambridge Ancient History ; Owman, A.K., Garnsey, P., Rathbone, D., Eds.; The high empire, AD 70-192; Cambridge University Press: Cambridge, UK, 2007; Volume 11, pp. 787–817. [ Google Scholar ]
• Ritchie, H.; Rodés-Guirao, L.; Mathieu, E.; Gerber, M.; Ortiz-Ospina, E.; Hasell, J.; Roser, M. Population Growth. Published online at OurWorldInData.org. 2023. Available online: https://ourworldindata.org/population-growth (accessed on 6 September 2023).
• Berche, P. Life and death of smallpox. Presse Méd. 2022 , 51 , 104117. [ Google Scholar ] [ CrossRef ]
• Sabbatani, S.; Fiorino, S. La peste antonina e il declino dell’Impero Romano. Ruolo della guerra partica e della guerra marcomannica tra il 164 e il 182 d.C. nella diffusione del contagio. Infez. Med. 2009 , 17 , 261–275. [ Google Scholar ]
• Suzuki, A. Smallpox and the epidemiological heritage of modern Japan: Towards a total history. Med. Hist. 2011 , 55 , 313–318. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Acuna-Soto, R.; Stahle, D.W.; Cleaveland, M.K.; Therrell, M.D. Megadrought and megadeath in 16th century Mexico. Emer. Infect. Dis. 2002 , 8 , 360–362. [ Google Scholar ] [ CrossRef ]
• Acuna-Soto, R.; Stahle, D.W.; Therrell, M.D.; Chavez, S.G.; Cleaveland, M.K. Drought, epidemic disease, and the fall of classic period cultures in Mesoamerica (AD 750–950). Hemorrhagic fevers as a cause of massive population loss. Med. Hypotheses 2005 , 65 , 405–409. [ Google Scholar ] [ CrossRef ]
• Vågene, A.J.; Herbig, A.; Campana, M.G.; García, N.M.R.; Warinner, C.; Sabin, S.; Spyrou, M.A.; Valtueña, A.A.; Huson, D.; Tuross, N.; et al. Salmonella enterica genomes from victims of a major sixteenth-century epidemic in Mexico. Nat. Ecol. Evol. 2018 , 2 , 520–528. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Glatter, K.A.; Finkelman, P. History of the plague: An ancient pandemic for the age of COVID-19. Am. J. Med. 2021 , 134 , 176–181. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Slavin, P. Death by the lake: Mortality crisis in early fourteenth-century central Asia. J. Interdiscipl. Hist. 2019 , 50 , 59–90. [ Google Scholar ] [ CrossRef ]
• Demeure, C.E.; Dussurget, O.; Fiol, G.M.; Le Guern, A.S.; Savin, C.; Pizarro-Cerdá, J. Yersinia pestis and plague: An updated view on evolution, virulence determinants, immune subversion, vaccination, and diagnostics. Genes Immun. 2019 , 20 , 357–370. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Andrianaivoarimanana, V.; Iharisoa, A.L.; Rahalison, L.; Ralimanantsoa, M.L.; Ratsitorahina, M.; Rakotonanahary, R.J.L.; Carniel, E.; Demeure, C.; Rajerison, M. Short- and long-term humoral immune response against Yersinia pestis in plague patients, Madagascar. BMC Inf. Dis. 2020 , 20 , 822. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mead, P.S. Yersinia species (including plague). In Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Diseases , 8th ed.; Bennett, J.E., Dolin, R., Blaser, M.J., Eds.; Elsevier: Amsterdam, The Netherlands, 2015; Volume 1, pp. 2607–2618. [ Google Scholar ]
• Titball, R.W.; Hill, J.; Lawton, D.G.; Brown, K.A. Yersinia pestis and plague. Biochemical. Soc. Trans. 2003 , 31 , 104–107. [ Google Scholar ] [ CrossRef ]
• Dennis, D.T.; Mead, P.S. Plague. In Tropical Infectious Diseases: Principles, Pathogens & Practice , 2nd ed.; Guerrant, R.L., Walker, D.H., Weller, P.F., Eds.; Elsevier: Amsterdam, The Netherlands, 2006; Volume 1, pp. 471–481. [ Google Scholar ]
• Mordechai, L.; Eisenberg, M.; Newfield, T.P.; Izdebski, A.; Kay, J.E.; Poinar, H. The Justinian Plague: An inconsequential pandemic? Proc. Natl. Acad. Sci. USA 2019 , 116 , 25546–25554. [ Google Scholar ] [ CrossRef ]
• Keller, M.; Spyrou, M.A.; Scheib, C.L.; Neumann, G.U.; Kröpelin, A.; Haas-Gebhard, B.; Päffgen, B.; Haberstroh, J.; Ribera i Lacomba, A.; Raynaud, C.; et al. Ancient Yersinia pestis genomes from across Western Europe reveal early diversification during the first pandemic (541–750). Proc. Natl. Acad. Sci. USA 2019 , 116 , 12363–12372. [ Google Scholar ] [ CrossRef ]
• Eroshenko, G.A.; Nosov, N.Y.; Krasnov, Y.M.; Oglodin, Y.G.; Kukleva, L.M.; Guseva, N.P.; Kuznetsov, A.A.; Abdikarimov, S.T.; Dzhaparova, A.K.; Kutyrev, V.V. Yersinia pestis strains of ancient phylogenetic branch 0.ANT are widely spread in the high- mountain plague foci of Kyrgyzstan. PLoS ONE 2017 , 12 , e0187230. [ Google Scholar ] [ CrossRef ]
• Eisenberg, M.; Mordechai, L. The Justinianic plague and global pandemics: The making of the plague concept. Am. Hist. Rev. 2020 , 125 , 1632–1667. [ Google Scholar ] [ CrossRef ]
• Spyrou, M.A.; Musralina, L.; Gnecchi Ruscone, G.A.; Kocher, A.; Borbone, P.G.; Khartanovish, V.I.; Buzhilova, A.; Djansugurova, L.; Bos, K.I.; Kühnert, D.; et al. The source of the Black Death in fourteenth-century central Eurasia. Nature 2022 , 606 , 718–724. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ziegler, P. The Black Death ; Readers Union/Collins: Glasgow, Scotland, 1969. [ Google Scholar ]
• Wang, H.-Q.; Chen, F.; Ermenbaev, B.; Satylkanov, R. Comparison of drought-sensitive tree-ring records from the Tien Shan of Kyrgyzstan and Xinjiang (China) during the last six centuries. Adv. Clim. Chang. Res. 2017 , 8 , 18–25. [ Google Scholar ] [ CrossRef ]
• Frith, J. The history of plague—Part 1. The three great pandemics. J. Mil. Veteran Health 2012 , 20 , 11–16. [ Google Scholar ]
• Benedictow, O.J. The Black Death, 1346–1353: The Complete History ; The Boydel Press: Woodbridge, UK, 2004. [ Google Scholar ]
• Izdebski, A.; Guzowski, P.; Poniat, R.; Masci, L.; Palli, J.; Vignola, C.; Bauch, M.; Cocozza, C.; Fernandes, R.; Ljungqvist, F.C.; et al. Palaeoecological data indicates land-use changes across Europe linked to spatial heterogeneity in mortality during the Black Death pandemic. Nat. Ecol. Evol. 2022 , 6 , 297–306. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bramanti, B.; Dean, K.R.; Walløe, L.; Chr Stenseth, N. The third plague pandemic in Europe. Proc. R. Soc. B 2019 , 286 , 20182429. [ Google Scholar ] [ CrossRef ]
• Mushtaq, M.U. Public health in British India: A brief account of the history of medical services and disease prevention in colonial India. Ind. J. Commun. Med. 2009 , 34 , 6–14. [ Google Scholar ] [ CrossRef ]
• Hawgood, B.J. Waldemar Mordecai Haffkine, CIE (1860–1930): Prophylactic vaccination against cholera and bubonic plague in British India. J. Med. Biogr. 2007 , 15 , 9–19. [ Google Scholar ] [ CrossRef ]
• Kanungo, S.; Azman, A.S.; Ramamurthy, T.; Deen, J.; Dutta, S. Cholera. Lancet 2022 , 399 , 1429–1440. [ Google Scholar ] [ CrossRef ]
• Gómez-Díaz, D. Cholera: First through third pandemics, 1816–1861. In Ncyclopedia of Pestilence, Pandemics, and Plagues ; Byrne, J., Ed.; Greenwood Press: Westport, CT, USA, 2008; Volume 1, pp. 96–105. [ Google Scholar ]
• Clemens, J.D.; Nair, G.B.; Ahmed, T.; Qadri, F.; Holmgren, J. Cholera. Lancet 2017 , 390 , 1539–1549. [ Google Scholar ] [ CrossRef ]
• Chowdhury, F.; Ross, A.G.; ISlam, M.T.; McMillan, N.A.J.; Qadri, F. Diagnosis, management, and future control of cholera. Clin. Microbiol. Rev. 2022 , 35 , e0021121. [ Google Scholar ] [ CrossRef ]
• Pollitzer, R. History of the Disease. In Cholera ; Pollitzer, R., Ed.; World Health Organization: Geneva, Switzerland, 1959; pp. 11–50. [ Google Scholar ]
• Ramamurthy, T.; Ghosh, A. A re-look at cholera pandemics from early times to now in the current Era of Epidemiology. J. Disaster Res. 2021 , 16 , 110–117. [ Google Scholar ] [ CrossRef ]
• Shousha, A.T. Cholera epidemic in Egypt (1947). Bull. World Health Organ. 1948 , 1 , 353–381. [ Google Scholar ] [ PubMed ]
• Lippi, D.; Gotuzzo, E.; Cain, S. Cholera. Microb. Spectr. 2016 , 4 . [ Google Scholar ] [ CrossRef ]
• World Health Organization [WHO]. Global Health Observatory (2022) ; World Health Organization: Geneva, Switzerland, 2023; Available online: https://www.who.int/data/gho (accessed on 8 April 2024).
• lic, I.; Ilic, M. Global patterns of trends in cholera mortality. Trop. Med. Infect. Dis. 2023 , 8 , 169. [ Google Scholar ] [ CrossRef ]
• Pappas, G.; Kiriaze, I.J.; Falagas, M.E. Insights into infectious disease in the era of Hippocrates. Int. J. Inf. Dis. 2008 , 12 , 347–350. [ Google Scholar ] [ CrossRef ]
• Javanian, M.; Barary, M.; Ghebrehewet, S.; Koppolu, V.; Vasigala, V.K.R.; Ebrahimpour, S. A brief review of influenza virus infection. J. Med. Virol. 2021 , 93 , 4638–4646. [ Google Scholar ] [ CrossRef ]
• International Committee on Taxonomy of Viruses [ICTV]. Current ICTV Taxonomy Release. 2024. Available online: https://ictv.global/taxonomy (accessed on 11 April 2024).
• Krammer, F.; Smith, G.J.D.; Touchier, R.A.M.; Peiris, M.; Kedzierska, K.; Doherty, P.C.; Palese, P.; Shaw, M.L.; Treanor, J.; Webster, R.G.; et al. Influenza. Nat. Rev. Dis. Primers 2018 , 4 , 3. [ Google Scholar ] [ CrossRef ]
• Petrone, M.E.; Parry, R.; Mifsud, J.C.O.; Brussel, K.V.; Voorhees, I.; Richards, Z.T.; Holmes, E.C. Evidence for an ancient aquatic origin of the RNA viral order Articulavirales. PNAS 2023 , 120 , e2310529120. [ Google Scholar ] [ CrossRef ]
• Iuliano, A.D.; Roguski, K.M.; Chang, H.H.; Muscatello, D.J.; Palekar, R.; Tempia, S.; Cohen, C.; Gran, J.M.; Schanzer, D.; Cowling, B.J.; et al. Estimates of global seasonal influenza-associated respiratory mortality: A modelling study. Lancet 2018 , 391 , 1285–1300. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Simonsen, L. The global impact of influenza on morbidity and mortality. Vaccine 1999 , 17 , S3–S10. [ Google Scholar ] [ CrossRef ]
• Paget, J.; Spreeuwenberg, P.; Charu, V.; Taylor, R.J.; Iuliano, A.D.; Bresee, J.; Simonsen, L.; Viboud, C.; Global Seasonal Influenza-associated Mortality Collaborator Network and GLaMOR Collaborating Teams. Global mortality associated with seasonal influenza epidemics: New burden estimates and predictors from the GLaMOR Project. J. Glob. Health 2019 , 9 , 020421. [ Google Scholar ] [ CrossRef ]
• Mills, C.E.; Robins, J.M.; Lipstick, M. Transmissibility of 1918 pandemic influenza. Nature 2004 , 432 , 904–906. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Eichel, O.R. The long-time cycles of pandemic influenza. J. Am. Stat. Assoc. 1922 , 18 , 446–454. [ Google Scholar ] [ CrossRef ]
• Patterson, K.D.; Pyle, G.F. The geography and mortality of the 1918 influenza pandemic. Bull. Hist. Med. 1991 , 65 , 4–21. [ Google Scholar ]
• Johnson, N.P.; Mueller, J. Updating the accounts: Global mortality of the 1918–1920 “Spanish” influenza pandemic. Bull. Hist. Med. 2002 , 76 , 105–115. [ Google Scholar ] [ CrossRef ]
• Dawood, F.S.; Iuliano, A.D.; Reed, C.; Meltzer, M.I.; Shay, D.K.; Cheng, P.Y.; Bandaranayake, D.; Breiman, R.F.; Brooks, W.A.; Buchy, P.; et al. Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: A modelling study. Lancet Infect. Dis. 2012 , 12 , 687–695. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Simonsen, L.; Spreeuwenberg, P.; Lustig, R.; Taylor, R.J.; Fleming, D.M.; Kroneman, M.; Van Kerkhove, M.D.; Mounts, A.W.; Paget, W.J.; GLaMOR Collaborating Teams. Global mortality estimates for the 2009 influenza pandemic from the GLaMOR Project: A modeling study. PLoS Med. 2013 , 10 , e1001558. [ Google Scholar ] [ CrossRef ]
• Spreeuwenberg, P.; Kroneman, M.; Paget, J. Reassessing the global mortality burden of the 1918 influenza pandemic. Am. J. Epidemiol. 2018 , 187 , 2561–2567. [ Google Scholar ] [ CrossRef ]
• Berche, P. The enigma of the 1889 Russian flu pandemic: A coronavirus? Press. Méd. 2022 , 51 , 104111. [ Google Scholar ] [ CrossRef ]
• Tumpey, T.M.; Basler, C.F.; Aguilar, P.V.; Zeng, H.; Solórzano, A.; Swayne, D.E.; Cox, N.J.; Katz, J.M.; Taubenberger, J.K.; Palese, P.; et al. Characterization of the reconstructed 1918 Spanish influenza pandemic virus. Science 2005 , 301 , 77–80. [ Google Scholar ] [ CrossRef ]
• Barry, J.M. The site of origin of the 1918 influenza pandemic and its public health implications. J. Transl. Med. 2004 , 2 , 3. [ Google Scholar ] [ CrossRef ]
• James, S.H.; Whitley, R.J. Influenza viruses. In Infectious Diseases , 4th ed.; Cohen, J., Powderly, W.G., Opal, S.M., Eds.; Elsevier: Amsterdam, The Netherlands, 2017. [ Google Scholar ]
• Kilbourne, E.D. Influenza Pandemics of the 20th century. Emerg. Inf. Dis. 2006 , 12 , 9–14. [ Google Scholar ] [ CrossRef ]
• Fouchier, R.A.M.; Osterhaus, A.D.M.E.; Brown, I.H. Animal influenza virus surveillance. Vaccine 2003 , 21 , 1754–1757. [ Google Scholar ] [ CrossRef ]
• Zhang, R.; Dong, X.; Wang, D.; Feng, L.; Zhou, L.; Ren, R.; Greene, C.; Song, Y.; Millman, A.J.; Azziz-Baumgartner, E.; et al. One hundred years of influenza since the 1918 pandemic—Is China prepared today? CCDC Wkly. 2019 , 1 , 56–61. [ Google Scholar ] [ CrossRef ]
• Payne, A.M.-M.; McDonald, J.C. Symposium on the Asian influenza epidemic, 1957. Proc. R. Soc. Med. 1958 , 51 , 1009–1018. [ Google Scholar ]
• Honigsbaum, M. Revisiting the 1957 and 1968 influenza pandemics. Lancet 2020 , 395 , 13–19. [ Google Scholar ] [ CrossRef ]
• Viboud, C.; Simonsen, L.; Fuentes, R.; Flores, J.; Miller, M.A.; Chowell, G. Global mortality impact of the 1957–1959 influenza pandemic. J. Inf. Dis. 2016 , 213 , 738–745. [ Google Scholar ] [ CrossRef ]
• Francis, T., Jr.; Salk, J.E.; Pearson, H.E.; Brown, P.N. Protective effect of vaccination against induced influenza A. J. Clin. Investig. 1945 , 24 , 536–546. [ Google Scholar ] [ CrossRef ]
• Cockburn, W.C.; Delon, P.J.; Ferreira, W. Origin and progress of the 1968–1969 Hong Kong influenza pandemic. Bull. World Health Organ. 1969 , 41 , 345–348. [ Google Scholar ]
• Bajardi, P.; Poletto, C.; Ramasco, J.J.; Tizzoni, M.; Colizza, V.; Vespignani, A. Human mobility networks, travel restrictions, and global spread of 2009 H1N1 pandemic. PLoS ONE 2011 , 6 , e16591. [ Google Scholar ] [ CrossRef ]
• Hajjar, S.A.; McIntosh, K. The first influenza pandemic of the 21st century. Ann. Saudi Med. 2010 , 30 , 1–10. [ Google Scholar ] [ CrossRef ]
• Faria, N.R.; Rambaut, A.; Suchard, M.A.; Baele, G.; Bedford, T.; Ward, M.J.; Tatem, A.J.; Sousa, J.D.; Arinaminpathy, N.; Pépin, J.; et al. HIV epidemiology. The early spread and epidemic ignition of HIV-1 in human populations. Science 2014 , 346 , 56–61. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Gryseels, S.; Watts, T.D.; Kabongo Mpolesha, J.M.; Larsen, B.B.; Lemey, P.; Muyembe-Tamfum, J.J.; Teuwen, D.E.; Worobey, M. A near full-length HIV-1 genome from 1966 recovered from formalin-fixed paraffin-embedded tissue. Proc. Natl. Acad. Sci. USA 2000 , 117 , 12222–12229. [ Google Scholar ] [ CrossRef ]
• Sharp, P.M.; Hahn, B.H. Origins of HIV and the AIDS pandemic. Cold Spring Harb. Prospect. Med. 2011 , 1 , a006841. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Rupp, S.; Ambata, P.; Narat, V.; Giles-Vernick, T. Bedoend the cut hunter: A historical epidemiology of HIV beginnings in Central Africa. EcoHealth 2016 , 13 , 661–671. [ Google Scholar ] [ CrossRef ]
• Quammen, D. The Chimp and the River: How AIDS Emerged from an African Forest ; WW Norton & Company: New York, NY, USA, 2015. [ Google Scholar ]
• Wertheim, J.O.; Chu, D.K.W.; Peiris, J.S.M.; Kosakovsky, S.L.; Poon, L.L.M. A case for the ancient origin of coronaviruses. J. Virol. 2013 , 87 , 7039–7045. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, Y.; Liu, Q.; Goo, D. Emerging coronaviruses: Genome structure, replication, and pathogenesis. J. Med. Virol. 2020 , 92 , 418–423. [ Google Scholar ] [ PubMed ]
• Latinne, A.; Hu, B.; Olival, K.J.; Zhu, G.; Zhang, L.; Li, H.; Chmura, A.A.; Field, H.E.; Zambrana-Torrelio, C.; Epstein, J.H.; et al. Origin and cross-species transmission of bat coronaviruses in China. Nat. Commun. 2020 , 11 , 4235. [ Google Scholar ] [ CrossRef ]
• Fan, Y.; Zhao, K.; Shi, Z.-L.; Zhou, P. Bat coronaviruses in China. Viruses 2019 , 11 , 210. [ Google Scholar ] [ CrossRef ]
• Wong, A.C.P.; Li, X.; Lay, S.K.P.; Woo, P.C.Y. Global epidemiology of bat coronaviruses. Viruses 2019 , 11 , v11020174. [ Google Scholar ] [ CrossRef ]
• Corman, V.M.; Muth, D.; Niemeyer, D.; Drosten, C. Hosts and sources of endemic human coronaviruses. Adv. Virus. Res. 2018 , 100 , 163–188. [ Google Scholar ] [ PubMed ]
• Stadler, K.; Masignani, V.; Eickmann, M.; Becker, S.; Abrignani, S.; Klenk, H.-D.; Rappuoli, R. SARS—Beginning to understand a new virus. Nat. Rev. Micribiol. 2003 , 1 , 209–218. [ Google Scholar ] [ CrossRef ]
• World Health Organization [WHO]. Severe Acute Respiratory Syndrome (SARS) ; World Health Organization: Geneva, Switzerland, 2024; Available online: https://www.who.int/health-topics/severe-acute-respiratory-syndrome#tab=tab_2 (accessed on 15 April 2024).
• Aly, M.; Elrobh, M.; Alzayer, M.; Ajuhani, S.; Balkhy, H. Occurrence of the Middle East respiratory syndrome coronavirus (MERS-CoV) across the Gulf Corporation Council countries: Four years update. PLoS ONE 2017 , 12 , e0183850. [ Google Scholar ] [ CrossRef ]
• Hajjar, S.A.; Memish, Z.A.; McIntosh, K. Middle East respiratory syndrome coronavirus (MERS-CoV): A perpetual challenge. Ann. Saudi Med. 2013 , 33 , 427–436. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Su, S.; Wong, G.; Liu, Y.; Gao, G.F.; Li, S.; Bi, Y. MERS in South Korea and China: A potential outbreak threat? Lancet 2015 , 385 , 2349–2350. [ Google Scholar ] [ CrossRef ]
• World Health Organization [WHO]. Middle East Respiratory Syndrome: Global Summary and Assessment of Risk ; World Health Organization: Geneva, Switzerland, 2022; Available online: https://iris.who.int/bitstream/handle/10665/364525/WHO-MERS-RA-2022.1-eng.pdf?sequence=1 (accessed on 23 April 2024).
• Killerby, M.E.; Biggs, H.M.; Midgley, C.M.; Gerber, S.I.; Watson, J.T. Middle East respiratory syndrome coronavirus transmission. Emerg. Inf. Dis. 2020 , 26 , 191–198. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Alagaili, A.N.; Briese, T.; Mishra, N.; Kapoor, V.; Sameroff, S.C.; Burble, P.D.; Wit, E.; Munster, V.J.; Hensley, L.E.; Zalmout, I.S.; et al. Middle East respiratory syndrome coronavirus infection in dromedary camels in Saudi Arabia. mBio 2014 , 5 , e00884-14. [ Google Scholar ] [ CrossRef ]
• Chu, D.K.W.; Poon, L.L.M.; Gomaa, M.M.; Shehata, M.M.; Perera, R.A.P.M.; Zeid, D.A.; El Rifay, A.S.; Siu, L.Y.; Guan, Y.; Webby, R.J.; et al. MERS coronaviruses in dromedary camels, Egypt. Emerg. Inf. Dis. 2014 , 20 , 1049–1053. [ Google Scholar ] [ CrossRef ]
• Dias, R.A. Discutindo a origem do SARS-CoV-2 e as contribuições da medicina veterinária na prevenção de novas pandemias. Rev. CFMV 2020 , 84 , 8–13. [ Google Scholar ]
• World Health Organization [WHO]. WHO COVID-19 Dashboard ; World Health Organization: Geneva, Switzerland, 2024; Available online: https://data.who.int/dashboards/covid19 (accessed on 15 April 2024).
• Zhou, P.; Yang, X.L.; Wang, X.G.; Hu, B.; Zhang, L.; Zhang, W.; Si, H.R.; Zhu, Y.; Li, B.; Huang, C.L.; et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020 , 579 , 270–273. [ Google Scholar ] [ CrossRef ]
• Andersen, K.G.; Rambaut, A.; Lipkin, W.I.; Holmes, E.C.; Garry, R.F. The proximal origin of SARS-CoV-2. Nat. Med. 2020 , 26 , 450–452. [ Google Scholar ] [ CrossRef ]
• Lam, T.T.; Jia, N.; Zhang, Y.W.; Shum, M.H.; Jiang, J.F.; Zhu, H.C.; Tong, Y.G.; Shi, Y.X.; Ni, X.B.; Liao, Y.S.; et al. Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature 2020 , 583 , 282–285. [ Google Scholar ] [ CrossRef ]
• McAloose, D.; Laverack, M.; Wang, L.; Killian, M.L.; Caserta, L.C.; Yuan, F.; Mitchell, P.K.; Queen, K.; Mauldin, M.R.; Cronk, B.D.; et al. From people to Panthera: Natural SARS-CoV-2 infection in tigers and lions at the Bronx Zoo. mBio 2020 , 11 , e02220-20. [ Google Scholar ] [ CrossRef ]
• Munnink, B.B.; Sikkema, R.S.; Nieuwenhuijse, D.F.; Molenaar, R.J.; Munger, E.; Molenkamp, R.; van der Spek, A.; Tolsma, P.; Rietveld, A.; Brouwer, M.; et al. Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans. Science 2021 , 371 , 172–177. [ Google Scholar ] [ CrossRef ]
• Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020 , 395 , 497–506. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, Q.; Guan, X.; Wu, P.; Wang, X.; Zhou, L.; Tong, Y.; Ren, R.; Leung, K.S.M.; Lau, E.H.Y.; Wong, J.Y.; et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-Infected pneumonia. N. Engl. J. Med. 2020 , 382 , 1199–1207. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yu, W.B.; Tang, G.D.; Zhang, L.; Corlett, R.T. Decoding the evolution and transmissions of the novel pneumonia coronavirus (SARS-CoV-2/HCoV-19) using whole genomic data. Zool. Res. 2020 , 41 , 247–257. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Foster, P. Phylogenetic network analysis of SARS-CoV-2 genomes. PNAS 2020 , 117 , 9241–9243. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Barry, J.M. The Great Influenza: The Story of the Deadliest Pandemic in History ; Penguin Books: Westminster, UK, 2020. [ Google Scholar ]
• Wang, R.J.; Al-Saffar, S.I.; Rogers, J.; Hahn, M.W. Human generation times across the past 250,000 years. Sci. Adv. 2023 , 9 , eabm7047. [ Google Scholar ] [ CrossRef ]
• Ausubel, J.H.; Marchetti, C. The evolution of transport. Ind. Phys. 2001 , 7 , 20–24. [ Google Scholar ]
• Sakai, T.; Morimoto, Y. The History of Infectious Diseases and Medicine. Pathogens 2022 , 11 , 1147. [ Google Scholar ] [ CrossRef ]
• Hutchings, M.I.; Truman, A.W.; Wilkinson, B. Antibiotics: Past, present and future. Curr. Opin. Microbiol. 2019 , 51 , 72–80. [ Google Scholar ] [ CrossRef ]
• Jiang, S.; He, Y.; Liu, S. SARS vaccine development. Emerg. Infect. Dis. 2005 , 11 , 1016–1120. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Carrasco-Hernandez, R.; Jácome, R.; Vidal, Y.L.; de León, S.P. Are RNA viruses candidate agents for the next global pandemic? A review. ILAR J. 2017 , 58 , 343–358. [ Google Scholar ] [ CrossRef ]
• Laxminarayan, R. The overlooked pandemic of antimicrobial resistance. Lancet 2022 , 399 , 606–607. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wu, Z.; Harrich, D.; Li, Z.; Hu, D.; Li, D. The unique features of SARS-CoV-2 transmission: Comparison with SARS-CoV, MERS-CoV and 2009 H1N1 pandemic influenza virus. Rev. Med. Virol. 2020 , 31 , e2171. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Taylor, L.H.; Latham, S.M.; Woolhouse, M.E.J. Risk factors for human disease emergence. Philos. Trans. R. Soc. 2001 , 356 , 983–989. [ Google Scholar ] [ CrossRef ]
• Cleveland, S.; Laurenson, M.K.; Taylor, L.H. Disease of humans and their domestic mammals: Pathogen characteristics, host range and the risk of emergence. Philos. Trans. R. Soc. 2001 , 356 , 991–999. [ Google Scholar ] [ CrossRef ]
• Johnson, C.K.; Hitchers, P.L.; Evans, T.S.; Goldstein, T.; Thomas, K.; Clemens, A.; Joly, D.O.; Wolfe, N.D.; Daszak, P.; Karesh, W.B.; et al. Spillover and pandemic properties of zoonotic viruses with high host plasticity. Sci. Rep. 2015 , 5 , 14830. [ Google Scholar ]
• Han, B.A.; Krames, A.M.; Drake, J.M. Global patterns of zoonotic disease in mammals. Trends Parasitol. 2016 , 32 , 565–577. [ Google Scholar ] [ CrossRef ]
• Watsa, M.; Wildlife Disease Surveillance Focus Group. Rigorous wildlife disease surveillance. Science 2020 , 369 , 145–147. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Guarducci, G.; Porchia, B.R.; Lorenzini, C.; Nante, N. Overview of case definitions and contact tracing indications in the 2022 monkeypox outbreak. Infez. Med. 2022 , 31 , 13–19. [ Google Scholar ] [ PubMed ]

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Dias, R.A. Towards a Comprehensive Definition of Pandemics and Strategies for Prevention: A Historical Review and Future Perspectives. Microorganisms 2024 , 12 , 1802. https://doi.org/10.3390/microorganisms12091802

Dias RA. Towards a Comprehensive Definition of Pandemics and Strategies for Prevention: A Historical Review and Future Perspectives. Microorganisms . 2024; 12(9):1802. https://doi.org/10.3390/microorganisms12091802

Dias, Ricardo Augusto. 2024. "Towards a Comprehensive Definition of Pandemics and Strategies for Prevention: A Historical Review and Future Perspectives" Microorganisms 12, no. 9: 1802. https://doi.org/10.3390/microorganisms12091802

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