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Testing a Simple and Frugal Model of Health Protective Behaviour in Epidemic Times
Abstract
The COVID-19 epidemic highlighted the necessity to integrate dynamic human behaviour change into infectious disease transmission models. The adoption of health protective behaviour, such as handwashing or staying at home, depends on both epidemiological and personal variables. However, only a few models have been proposed in the recent literature to account for behavioural change in response to the health threat over time. This study aims to estimate the relevance of TELL ME, a simple and frugal agent-based model developed following the 2009 H1N1 outbreak to explain individual engagement in health protective behaviours in epidemic times and how communication can influence this. Basically, TELL ME includes a behavioural rule to simulate individual decisions to adopt health protective behaviours. To test this rule, we used behavioural data from a series of 12 cross-sectional surveys in France over a 6-month period (May to November 2020). Samples were representative of the French population (N=24,003). We found the TELL ME behavioural rule to be associated with a moderate to high error rate in representing the adoption of behaviours, indicating that parameter values are not constant over time and that other key variables influence individual decisions. These results highlight the crucial need for longitudinal behavioural data to better calibrate epidemiological models accounting for public responses to infectious disease threats.
... Perceived Benefits: During the pandemic, people's decisions to adopt protective measures against the disease are usually considered either exogenous or overlooked [19]. The risk of infection has generally been examined through the prism of population density and movement, ignoring the impact of social and health practices, despite the crucial role that interpersonal connections play in either raising or decreasing the transmission of respiratory infectious diseases [19]. ...
... Perceived Benefits: During the pandemic, people's decisions to adopt protective measures against the disease are usually considered either exogenous or overlooked [19]. The risk of infection has generally been examined through the prism of population density and movement, ignoring the impact of social and health practices, despite the crucial role that interpersonal connections play in either raising or decreasing the transmission of respiratory infectious diseases [19]. Given the pandemic situation and the potential for even more severe pandemics, it is essential to evaluate the importance and usefulness of health protective behaviors [19]. ...
... The risk of infection has generally been examined through the prism of population density and movement, ignoring the impact of social and health practices, despite the crucial role that interpersonal connections play in either raising or decreasing the transmission of respiratory infectious diseases [19]. Given the pandemic situation and the potential for even more severe pandemics, it is essential to evaluate the importance and usefulness of health protective behaviors [19]. In addition, the mediating effect of consumer innovativeness on the relationship between perceived benefits on health protective behaviors has not been well established in the past. ...
social media engagement; perceived benefits, consumer innovativeness; health protective behaviours; private healthcare marketing The research investigates the relationship between perceived benefits (PB) and consumer innovativeness (CI) in private healthcare, focusing on the influence of social media engagement. The pandemic has accelerated the shift toward digital communication, prompting the need for innovative healthcare delivery methods and improved consumer interactions. The study aims to understand how PB impact consumer innovativeness and how this relationship influences health protective behaviours among healthcare consumers. Conducted with 400 participants using purposive sampling due to pandemic restrictions, data were collected through online surveys employing validated Likert scales. Statistical analyses, including PLS-SEM, were utilized to assess the relationships, ensuring reliability and validity throughout the research process while upholding ethical standards, such as participant confidentiality and informed consent. The findings indicate a significant positive correlation between PB and CI, demonstrating that PB enhance customer interaction via social media engagement. The discussion highlights the necessity for healthcare providers to leverage social media effectively to communicate the value of their services. However, the study acknowledges limitations, including a narrow sample size and the effects of pandemic on participant interactions. Future research should broaden geographic scope, examine demographic differences, and assess the role of personality traits in CI. The research contributes to existing literature by emphasizing the essential role of PB and CI in fostering innovative consumer behaviours. It suggests actionable strategies for healthcare providers to enhance consumer engagement and promote health protective behaviours through effective social media use. The study offers valuable insights for improving healthcare communication and delivery in the evolving digital landscape.
... During the COVID-19 pandemic, people's decisions to adopt protective measures against the disease are usually considered either exogenous or overlooked [20]. The risk of infection has generally been examined through the prism of population density and movement, ignoring the impact of social and health practices, despite the crucial role those interpersonal connections play in either raising or decreasing the transmission of respiratory infectious diseases [20]. ...
... During the COVID-19 pandemic, people's decisions to adopt protective measures against the disease are usually considered either exogenous or overlooked [20]. The risk of infection has generally been examined through the prism of population density and movement, ignoring the impact of social and health practices, despite the crucial role those interpersonal connections play in either raising or decreasing the transmission of respiratory infectious diseases [20]. Given the current COVID-19 situation and the potential for even more severe pandemics, it is essential to evaluate the importance and usefulness of health protective behaviors [20]. ...
... The risk of infection has generally been examined through the prism of population density and movement, ignoring the impact of social and health practices, despite the crucial role those interpersonal connections play in either raising or decreasing the transmission of respiratory infectious diseases [20]. Given the current COVID-19 situation and the potential for even more severe pandemics, it is essential to evaluate the importance and usefulness of health protective behaviors [20]. In addition, the mediating effect of consumer innovativeness on the relationship between self-efficacy, perceived benefits, and behavioral beliefs on health protective behaviors has not been well established in the past. ...
... This behavioral heterogeneity played a major role in shaping the trajectory and burden of the pandemic [27,28,29]. Numerous modeling types,(including compartmental [27,29,30,31,32,33,34,35,36,37,38,39,40,41,42], agent-based [26,41,43,44,45], network [44,45,46,47,48,49], etc.,) have been used to gain insight and understanding on the role of human behavior changes (or heterogeneities) on the spread and control of the SARS-CoV-2 pandemic. These models typically assume that individual disease-related behavior change (such as adherence or non-adherence to public health interventions, reducing contacts, etc.) is motivated by a combination of disease-related metrics (such as the level of disease prevalence and intervention fatigue in their local community or population) [33,35,38,40], peer pressure or influence [26,38,43,46], and other costs associated with adoption of disease-related precautionary behaviors). ...
... Numerous modeling types,(including compartmental [27,29,30,31,32,33,34,35,36,37,38,39,40,41,42], agent-based [26,41,43,44,45], network [44,45,46,47,48,49], etc.,) have been used to gain insight and understanding on the role of human behavior changes (or heterogeneities) on the spread and control of the SARS-CoV-2 pandemic. These models typically assume that individual disease-related behavior change (such as adherence or non-adherence to public health interventions, reducing contacts, etc.) is motivated by a combination of disease-related metrics (such as the level of disease prevalence and intervention fatigue in their local community or population) [33,35,38,40], peer pressure or influence [26,38,43,46], and other costs associated with adoption of disease-related precautionary behaviors). For example, the compartmental model developed by Pisaneschi et al. [34] accounts for average daily wages, assuming that a lower wage will force individuals to abandon disease-related precautionary behaviors. ...
A novel behavior-epidemiology model, which considers n heterogeneous behavioral groups based on level of risk tolerance and distinguishes behavioral changes by social and disease-related motivations (such as peer-influence and fear of disease-related hospitalizations), is developed. In addition to rigorously analyzing the basic qualitative features of this model, a special case is considered where the total population is stratified into two groups: risk-averse (Group 1) and risk-tolerant (Group 2). The two-group behavior model has three disease-free equilibria in the absence of disease, and their stability is analyzed using standard linearization and the properties of Metzler-stable matrices. Furthermore, the two-group model was calibrated and validated using daily hospitalization data for New York City during the first wave, and the calibrated model was used to predict the data for the second wave. Numerical simulations of the calibrated two-group behavior model showed that while the dynamics of the SARS-CoV-2 pandemic during the first wave was largely influenced by the behavior of the risk-tolerant individuals, the dynamics during the second wave was influenced by the behavior of individuals in both groups. It was also shown that disease-motivated behavioral changes had greater influence in significantly reducing SARS-CoV-2 morbidity and mortality than behavior changes due to the level of peer or social influence or pressure. Finally, it is shown that the initial proportion of members in the community that are risk-averse (i.e., the proportion of individuals in Group 1 at the beginning of the pandemic) and the early and effective implementation of non-pharmaceutical interventions have major impacts in reducing the size and burden of the pandemic (particularly the total SARS-CoV-2 mortality in New York City during the second wave).
... The current study's findings demonstrated that, following the intervention, there was a statistically significant difference in attitudes between the two groups, with the experimental group's average attitude being greater than that of the control group. This conclusion is in line with the findings of investigations conducted by Hedayati et al., Zaildo et al., Calcagni et al., and Lapoirie et al. [29][30][31][32]. In justification of this finding, it can be stated that training can make the educated person aware of the disease, and increasing knowledge can lead to an improved attitude, and a person accepts that with an increasing attitude towards infectious diseases, he can better observe self-care. ...
... This shift in attitude can be attributed to the increased knowledge acquired through the educational program. As individuals became more informed about the disease and its implications, they developed a more positive attitude towards adopting [29][30][31][32]. A positive attitude is crucial for promoting compliance with preventive measures and fostering a culture of health and safety within the community. ...
Background
COVID-19 is one of the most common diseases in recent years, the most important way to prevent is through self-care behaviors; therefore, it is important to these behaviors in people. According to the importance of promoting self-care behaviors of this disease, and according to the characteristics and effectiveness of interventions based on behavior change, this study aimed to investigate the effect of educational intervention on self-care behaviors of COVID-19 in a group of patients.
Methods
This quasi-experimental study was conducted on 164 people who referred to health and treatment centers in Dehdasht City, Iran. The cluster sampling method divided the participants into experimental and control groups at random (82 people for each group). Data collection tool was a researcher-made questionnaire completed by the control and experimental groups before and three months after the intervention. The intervention program in this training group is to form a WhatsApp group and send messages in the form of audio files, text messages, text messages with photos, video messages, and PowerPoints. After creating the group and adding the participants, according to the agreement with the group members, every day of the week (8:00 am to 12:00 pm) to send educational files through the WhatsApp application. Also, the group members could ask their questions and problems to the researcher during the designated hours. The control group was also given routine care and follow-up at the centers, and no training was given regarding self-care behaviors. After entering the SPSS 24, data were analyzed by independent t, chi-square, and paired t statistical tests.
Results
164 individuals working in healthcare services from health and treatment centers were included in this study. Before the intervention, demographic characteristics such as marital status, education level, medical history, and smoking history were similar between the two groups (P > 0.05), as indicated by the results of chi-square tests. Furthermore, there were no significant differences in the mean scores of knowledge, attitude, and self-care behaviors between the experimental and control groups prior to the intervention (P > 0.05), according to independent t-tests. Following the intervention, notable changes were observed. The post-intervention analysis revealed statistically significant differences between the experimental and control groups in terms of knowledge, attitude, and self-care behaviors (P = 0.001). Specifically, the experimental group exhibited significant improvements in these variables compared to the control group.
Conclusion
In this study, education led to the improvement of self-care behaviors in people who referred to health centers. Considering the importance of the role of health education in promoting self-care behaviors as well as preventing infectious diseases such as COVID-19, it is suggested that educational interventions focus on self-care behaviors in other diseases.
... In line with our results, the studies by Kim et al., 28 Ochie et al., 29 and Tu et al. 30 also highlight the major contribution of knowledge between knowledge and the constructs of the PMT. Also, perceived cost had a significant relationship with preventive behavior, which is consistent with the results of studies that was conducted with the aim of assessing the constructs of the PMT in preventive behaviors of respiratory infections and especially COVID-19, by Grano et al., 31 Navabi et al., 32 Martin-Lapoirie et al., 33 and Calcagni et al. 34 Due to the fact that the subjects had passed all the training courses to prevent respiratory infections, all the participants were also infected with some kind of respiratory infection. Given the work environment, the way respiratory infections are transmitted is completely normal; every day, subjects face a large number of patients suffering from respiratory infections who are easily infected. ...
Background
One of the most crucial and essential methods for the prevention and management of respiratory infections is for healthcare professionals to take precautions for their own safety. Using Protection Motivation Theory (PMT), the current study looked into effective elements influencing the staff at Kazeroon's Valiasr Hospital's preventive actions against respiratory diseases.
Methods
One hundred ninety‐two male and 108 female employees of the Valiasr Hospital in Kazeroon, Iran, participated in this cross‐sectional study, in May 2022. Census data were used as the sample technique. A questionnaire based on the PMT and a questionnaire collecting demographic data served as the data collection method. The study's content validity was confirmed by 10 health education experts, and its reliability was assessed using internal consistency techniques, resulting in a Cronbach's alpha coefficient of 0.87.The statistical program SPSS 24 was used to examine the data using the independent t test, logistic regression, and Pearson correlation.
Results
The average age was 34.11 ± 8.91 for men and 32.77 ± 6.09 for women. The majority of participants were married (73.3%), had university education (76.7%), and earned a monthly income between 10 and 15 million Tomans (75%). Notably, 97.7% of participants had received the COVID‐19 vaccine, and 77.7% had undergone training related to respiratory infections. The most common preventive practices included avoiding touching the eyes, noses, or mouths, wearing appropriate protective gear, and maintaining a safe distance of 1–2 m from others. Analysis of PMT constructs showed that participants had a generally positive perception toward preventive behaviors. Perceived vulnerability (P = 0.02), perceived cost (P = 0.03), and motivation (P = 0.001) were the three analyzed components that had the greatest impact on respiratory infection preventative behavior. Logistic regression revealed that perceived susceptibility, cost, and motivation significantly predicted the prevention of respiratory infections, with a predictive power of 45%. These findings highlight the importance of understanding the factors influencing preventive behaviors among hospital staff, from respiratory infections like COVID‐19.
Conclusion
According to the findings, the personnel at Kazeroon's Valiasr Hospital wore gloves, goggles, and other appropriate personal protective equipment. The individuals' decision to wear personal protection equipment was also impacted by perceived susceptibility, cost, and motivation.
... In the conceptual framework of PMT, the social network plays an important role by presenting an environmental source that can affect decisions, e.g., in the form of verbal persuasion or observational learning, such as in Hear et al. (Haer et al., 2016) in the context of flood risk management and the effect of different communication strategies and countermeasures based on communication (Badham and Gilbert, 2015). Mostly, PMT is used in connection with climate-related decisions, e.g., with regard to the implementation of preventive measures (see, e.g., (Wens et al., 2020;McEligot et al., 2019)) and the general vulnerability to the consequences of climate change (Krömker et al., 2008), or in health protective behavior in the context of infectious diseases ((Abdulkareem et al., 2018;Martin-Lapoirie et al., 2023)). ...
Representing and emulating human decision-making processes in artificial intelligence systems is a challenging task. This is because both internal (such as attitude, perceived health or motivation) and external factors (such as the opinions of others) and their mutual interactions affect decision-making. Modelling agents capable of human-like behavior, including undesirable actions, is an interesting use case for designing different AI-systems when it comes to human-AI-interactions and similar scenarios. However, agent-based decision-models in this domain tend to reflect the complex interplay of these factors only to a limited extent. To overcome this, we enrich these approaches with an agent architecture inspired by theories from psychology and sociology. Using human health behavior, specifically smoking, as a case study, we propose an agent-based approach to combine social pressure within Protection Motivation Theory (PMT) to allow for a theory-based representation of potentially harmful behavior including both internal and external factors. Based on smoking in social settings, we present experiments to demonstrate the model's capability to simulate human health behavior and the mutual influences between the selected concepts. In this use case, the resulting model has shown that social pressure is a driving influence in the observable system dynamics.
... It is natural that people seek to reduce unnecessary costs, and preventing respiratory infections can be an unwanted cost. These findings are in line with the results of studies by Calcagni et al. (42) and Lapoirie et al. (43). ...
Background
Hospital staff represent a vulnerable population for respiratory diseases. Consequently, the implementation of training programs becomes imperative as a preventive measure against such infections in these populations. The current study was conducted to examine the impact of an educational intervention based on the Protection Motivation Theory (PMT) on preventive behaviors for respiratory infections among a group of hospital staff.
Methods
This experimental study involves a sample of 150 hospital staff from Gachsaran City, Iran, in 2021–2022. The sampling technique involved the utilization of a random assignment approach to allocate individuals into two distinct groups: the experimental group, consisting of 75 participants, and the control group, also including 75 individuals. The data collection instrument was a questionnaire designed in accordance with the PMT. This questionnaire was administered to both the experimental and control groups prior to the intervention as well as two months following the intervention. The intervention program consisted of a total of five sessions, each lasting for 60 min, for the experimental group. These sessions were conducted on a weekly basis over a period of two and a half months. Specifically, there were two sessions held every month and one session held every two weeks. Following the completion of the program, the data was entered into SPSS-24 statistical software for analysis using paired t-tests, independent t-tests, and chi-square tests.
Results
The results indicated that prior to the intervention, there was no significant difference between the two groups in terms of perceived vulnerability constructs (p = 0.25), perceived severity (p = 0.63), perceived response (p = 0.32), and perceived reward (p = 0.11). Besides, there was no considerable distinction in perceived self-efficacy (p = 0.84), perceived response cost (p = 0.33), fear (p = 0.45), behavior motivation (p = 0.51), knowledge (p = 92), or vaccination behavior (p = 0.12) before the educational intervention. However, a significant change was noticed in each of the mentioned variables between the two groups after the intervention (p < 0.05).
Conclusion
The results of this study indicated that the implementation of an educational intervention grounded in the PMT yields positive outcomes in enhancing preventative behaviors pertaining to respiratory infections. Hence, it is recommended to utilize an intervention grounded in this theory among hospital staff as a viable approach to mitigating the occurrence of respiratory infections.
... However, the role of such a context in the decisions that people make to prevent or control the risk of infection during epidemics or pandemics remains largely unknown. Moreover, while epidemiologists and biomodelers have developed increasingly sophisticated epidemiological models to predict the spread of infectious diseases, these models are still insufficiently based on individual human behaviours (Lorig et al., 2021;Martin-Lapoirie et al., 2023;Verelst et al., 2016). Instead, individual decisions to adopt Health Protective Behaviour (HPB) for preventing or reducing health risks are simply ignored or represented by an exogenous proportion of the population which is considered as protected, independently from the evolution of the infection risk over time. ...
Four competing theories of health-protective behavior are reviewed: the health belief model, the theory of reasoned action, protection motivation theory, and subjective expected utility theory. In spite of their commonalities, these models are seldom tested against one another. The review points out the similarities and differences among these theories and the data and analyses needed to compare them. In addition to describing the content of the models, their conceptualization of key variables, and the combinatorial rules used to make predictions, some general problems in theory development and testing for health behaviors are examined. The article's goal is to help investigators design studies that will clarify the strengths and weaknesses of these models, leading toward a better understanding of health behavior.
COVID-19 in the UK has been characterised by periods of exponential growth and decline, as different non-pharmaceutical interventions (NPIs) are brought into play. During the early uncontrolled phase of the outbreak (March 2020) there was a period of prolonged exponential growth with epidemiological observations such as hospitalisation doubling every 3-4 days. The enforcement of strict lockdown measures led to a noticeable decline in all epidemic quantities that slowed during the summer as control measures were relaxed. From August 2020, infections, hospitalisations and deaths began rising once more and various NPIs were applied locally throughout the UK in response.
Controlling any rise in infection is a compromise between public health and societal costs, with more stringent NPIs reducing cases but damaging the economy and restricting freedoms. Typically, NPI imposition is made in response to the epidemiological state, are of indefinite length and are often imposed at short notice, greatly increasing the negative impact. An alternative approach is to consider planned, limited duration periods of strict NPIs aiming to purposefully reduce prevalence before such emergency NPIs are required. These “precautionary breaks” may offer a means of keeping control of the epidemic, while their fixed duration and the forewarning may limit their societal impact. Here, using simple analysis and age-structured models matched to the UK SARS-CoV-2 epidemic, we investigate the action of precautionary breaks. In particular we consider their impact on the prevalence of SARS-CoV-2 infection, as well as the total number of predicted hospitalisations and deaths caused by COVID-19 disease. We find that precautionary breaks provide the biggest gains when the growth rate is low, but offer a much needed brake on increasing infection when the growth rate is higher, potentially allowing other measures to regain control.
As the COVID-19 pandemic lingers, the possibility of ‘pandemic fatigue’ has raised worldwide concerns. Here, we examine whether there was a gradual reduction in adherence to protective behaviours against COVID-19 from March through December 2020, as hypothesized in expectations of fatigue. We considered self-report behaviours from representative samples of the populations of 14 countries (N = 238,797), as well as mobility and policy data for 124 countries. Our results show that changes in adherence were empirically meaningful and geographically widespread. While a low-cost and habituating behaviour (mask wearing) exhibited a linear rise in adherence, high-cost and sensitizing behaviours (physical distancing) declined, but this decline decelerated over time, with small rebounds seen in later months. Reductions in adherence to physical distancing showed little difference across societal groups, but were less intense in countries with high interpersonal trust. Alternative underlying mechanisms and policy implications are discussed.
This paper uses two thought experiments to argue that the complexity of the systems to which agent-based models (ABMs) are often applied is not the central source of difficulties ABMs have with prediction. We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful. ‘Wickedness’ is argued to be a more significant challenge to prediction than complexity. Critically, however, neither complexity nor wickedness makes prediction theoretically impossible in the sense of being formally undecidable computationally-speaking: intractable being the more apt term given the exponential sizes of the spaces being searched. However, endogenous ontological novelty in wicked systems is shown to render prediction futile beyond the immediately short term.
Social and behavioural factors are critical to the emergence, spread and containment of human disease, and are key determinants of the course, duration and outcomes of disease outbreaks. Recent epidemics of Ebola in West Africa and coronavirus disease 2019 (COVID-19) globally have reinforced the importance of developing infectious disease models that better integrate social and behavioural dynamics and theories. Meanwhile, the growth in capacity, coordination and prioritization of social science research and of risk communication and community engagement (RCCE) practice within the current pandemic response provides an opportunity for collaboration among epidemiological modellers, social scientists and RCCE practitioners towards a mutually beneficial research and practice agenda. Here, we provide a review of the current modelling methodologies and describe the challenges and opportunities for integrating them with social science research and RCCE practice. Finally, we set out an agenda for advancing transdisciplinary collaboration for integrated disease modelling and for more robust policy and practice for reducing disease transmission. Social and behavioural factors impact the emergence, spread and control of human disease. This paper reviews current disease modelling methodologies and the challenges and opportunities for integration with data from social science research and risk communication and community engagement practice.
Several indices can predict the long-term fate of emerging infectious diseases and the effect of their containment measures, including a variety of reproduction numbers (e.g. R 0 ). Other indices evaluate the potential for transient increases of epidemics eventually doomed to disappearance, based on generalized reactivity analysis. They identify conditions for perturbations to a stable disease-free equilibrium ( R 0 < 1 ) to grow, possibly causing significant damage. Here, we introduce the epidemicity index e 0 , a threshold-type indicator: if e 0 > 0, initial foci may cause infection peaks even if R 0 < 1 . Therefore, effective containment measures should achieve a negative epidemicity index. We use spatially explicit models to rank containment measures for projected evolutions of the ongoing pandemic in Italy. There, we show that, while the effective reproduction number was below one for a sizable timespan, epidemicity remained positive, allowing recurrent infection flare-ups well before the major epidemic rebounding observed in the fall.
Objectives
Without pharmaceutical measures available, endorsement of protective behaviours, such as hygiene behaviours, social distancing, and adherence to recommended behaviours in case of symptoms is of key importance to curb the ongoing COVID‐19 pandemic. Based on an extended version of the protection motivation theory, this study examined the role of perceived risks to oneself and to others, self‐efficacy, response efficacy, and perceived social norms for intentions to and the endorsement of several protective behaviours and alternative behaviours known to be ineffective. Further, it was hypothesised that effects of risk perceptions depended on high levels of self‐efficacy.
Design
Data were collected by telephone at the beginning of the lockdown in Switzerland with a large sample (N = 1,009) representative of the adult Swiss population.
Methods
All predictors (self‐efficacy, response efficacy, perceived social norms, intentions) but risk perceptions were assessed for hygiene behaviours, social distancing, adherence to recommended behaviours in case of symptoms, and alternative measures known to be ineffective.
Results
Across all analyses of intentions for and endorsement of protective and alternative behaviours, response efficacy and self‐efficacy emerged as the most important predictors. Social norms were mainly related to intentions, but not to behaviours. The different risk perceptions were rarely and inconsistently related to intentions and behaviours. No consistent pattern of interactions between self‐efficacy and risk perceptions emerged.
Conclusion
This study demonstrates that even in the face of a pandemic of an unknown virus, the resources (self‐efficacy, response efficacy) rather than the risk perceptions have the potential to promote protective behaviours.
Objective
To investigate rates of adherence to the UK’s test, trace, and isolate system over the initial 11 months of the covid-19 pandemic.
Design
Series of cross sectional online surveys.
Setting
37 nationally representative surveys in the UK, 2 March 2020 to 27 January 2021.
Participants
74 697 responses from 53 880 people living in the UK, aged 16 years or older (37 survey waves, about 2000 participants in each wave).
Main outcome measures
Identification of the main symptoms of covid-19 (cough, high temperature or fever, and loss of sense of smell or taste), self-reported adherence to self-isolation if symptoms were present and intention to self-isolate if symptoms were to develop, requesting a test for covid-19 if symptoms were present and intention to request a test if symptoms were to develop, and intention to share details of close contacts.
Results
Only 51.5% of participants (95% confidence interval 51.0% to 51.9%, n=26 030/50 570) identified the main symptoms of covid-19; the corresponding values in the most recent wave of data collection (25-27 January 2021) were 50.8% (48.6% to 53.0%, n=1019/2007). Across all waves, duration adjusted adherence to full self-isolation was 42.5% (95% confidence interval 39.7% to 45.2%, n=515/1213); in the most recent wave of data collection (25-27 January 2021), it was 51.8% (40.8% to 62.8%, n=43/83). Across all waves, requesting a test for covid-19 was 18.0% (95% confidence interval 16.6% to 19.3%, n=552/3068), increasing to 22.2% (14.6% to 29.9%, n=26/117) from 25 to 27 January. Across all waves, intention to share details of close contacts was 79.1% (95% confidence interval 78.8% to 79.5%, n=36 145/45 680), increasing to 81.9% (80.1% to 83.6%, n=1547/1890) from 25 to 27 January. Non-adherence was associated with being male, younger age, having a dependent child in the household, lower socioeconomic grade, greater financial hardship during the pandemic, and working in a key sector.
Conclusions
Levels of adherence to test, trace, and isolate are low, although some improvement has occurred over time. Practical support and financial reimbursement are likely to improve adherence. Targeting messaging and policies to men, younger age groups, and key workers might also be necessary.
In this study, we present results from five cross-sectional surveys on public risk perception of COVID-19 and its association with health protective behaviours in the UK over a 10-month period (March 2020 to January 2021). Samples were nationally balanced on age, gender, and ethnicity (total N = 6,281). We find that although risk perception varies between the time points surveyed, it is consistently, significantly, and positively correlated with the reported adoption of protective health behaviours, such as wearing face masks or social distancing. There is also an increase in reported health protective behaviours in the UK between March 2020 and January 2021. The strength of the association between risk perception and behaviour varies by time point, with a stronger relationship in January 2021 compared to March and May 2020. We also assess the stability of the psychological determinants of risk perception over time. People’s prosocial tendencies and individualistic worldviews, experience with the virus, trust in government, science, and medical professionals, as well as personal and collective efficacy all emerged as significant predictors. With few exceptions, these predictors remained consistent in their relationship with risk perception over time. Lastly, we find that psychological factors are more predictive of risk perception than an objective measure of situational severity, i.e. the number of confirmed COVID-19 cases at the time of data collection. Implications for risk communication are discussed.
Given the role of human behavior in the spread of disease, it is vital to understand what drives people to engage in or refrain from health-related behaviors during a pandemic. This paper examines factors associated with the adoption of self-protective health behaviors, such as social distancing and mask wearing, at the start of the Covid-19 pandemic in the USA. These behaviors not only reduce an individual’s own risk of infection but also limit the spread of disease to others. Despite these dual benefits, universal adoption of these behaviors is not assured. We focus on the role of socioeconomic differences in explaining behavior, relying on data collected in April 2020 during the early stages of the Covid-19 pandemic. The data include information on income, gender and race along with unique variables relevant to the current pandemic, such as work arrangements and housing quality. We find that higher income is associated with larger changes in self-protective behaviors. These gradients are partially explained by the fact that people with less income are more likely to report circumstances that make adopting self-protective behaviors more difficult, such as an inability to tele-work. Both in the USA and elsewhere, policies that assume universal compliance with self-protective measures—or that otherwise do not account for socioeconomic differences in the costs of doing so—are unlikely to be effective or sustainable.
The COVID-19 pandemic imposes new challenges on the capability of governments in intervening with the information dissemination and reducing the risk of infection outbreak. To reveal the complexity behind government intervention decision, we build a bi-layer network diffusion model for the information-disease dynamics that were intervened in and conduct a full space simulation to illustrate the trade-off faced by governments between information disclosing and blocking. The simulation results show that governments prioritize the accuracy of disclosed information over the disclosing speed when there is a high-level medical recognition of the virus and a high public health awareness, while, for the opposite situation, more strict information blocking is preferred. Furthermore, an unaccountable government tends to delay disclosing, a risk-averse government prefers a total blocking, and a low government credibility will discount the effect of information disclosing and aggravate the situation. These findings suggest that information intervention is indispensable for containing the outbreak of infectious disease, but its effectiveness depends on a complicated way on both external social/epidemic factors and the governments’ internal preferences and governance capability, for which more thorough investigations are needed in the future.
As countries in Europe gradually relaxed lockdown restrictions after the first wave, test-trace-isolate strategies became critical to maintain COVID-19 viral activity at low levels1,2. Reviewing their shortcomings can provide elements to consider in light of the second wave currently underway in Europe. Here we estimate the rate of detection of COVID-19 symptomatic cases in France after lockdown through the use of virological³ and participatory syndromic⁴ surveillance data coupled with mathematical transmission models calibrated to regional hospitalizations². Our findings indicate that around 90,000 incident symptomatic infections, corresponding to 9 out 10 cases, were not ascertained by the surveillance system in the first 7 weeks following lockdown from May 11 to June 28 2020, although the test positivity rate did not exceed WHO recommendations (5%)⁵. The median detection rate increased from 7% [6-8]% to 38% [35-44]% over time, with large regional variations, owing to a strengthening of the system as well as a decrease of epidemic activity. According to participatory surveillance data, only 31% of individuals with COVID-19-like symptoms consulted a doctor in the study period. This suggests that large numbers of symptomatic COVID-19 cases did not seek medical advice despite recommendations, as confirmed by serological studies6,7. Encouraging awareness and same-day healthcare-seeking behavior in suspect cases is critical to improve detection. However, the capacity of the system remained insufficient even at the low levels of viral circulation achieved after lockdown, and was predicted to deteriorate rapidly with increasing epidemic activity. Substantially more aggressive, targeted, and efficient testing with easier access is required to act as a pandemic-fighting tool. Testing strategy will be once again of critical value to lift current restrictive measures in Europe and avoid a third wave.
In absence of effective pharmaceutical treatments, the individual's compliance with a series of behavioral recommendations provided by the public health authorities play a critical role in the control and prevention of SARS-CoV2 infection. However, we still do not know much about the rate and determinants of adoption of the recommended health behaviors. This paper examines the compliance with the main behavioral recommendations, and compares sociocultural, psychosocial, and social cognitive explanations for its variation in the French population. Based on the current literature, these 3 categories of factors were identified as potential determinants of individual differences in the health preventive behaviors. The data used for these analyses are drawn from 2 cross-sectional studies (N = 2,000 in survey 1 and 2,003 in survey 2) conducted after the lockdown and before the peak of the COVID-19 epidemic in France. The participants were drawn from a larger internet consumer panel where recruitment was stratified to generate a socio-demographically representative sample of the French adult population. Overall, the results show a very high rate of compliance with the behavioral recommendations among the participants. A hierarchical regression analysis was then performed to assess the potential explanatory power of these approaches in complying with these recommendations by successively entering sociocultural factors, psychosocial factors, social cognitive factors in the model. Only the inclusion of the cognitive variables substantially increased the explained variance of the self-reported adoption of preventive behaviors (R² change = 23% in survey 1 and 2), providing better support for the social cognitive than the sociocultural and psychosocial explanations.
Background
Individual protective behaviors play an important role in the control of the spread of infectious diseases. This study aimed to investigate the adoption of protective behaviors by Chinese citizens amid the COVID-19 outbreak and its associated factors.
Methods
An online cross-sectional survey was conducted from 22 January to 14 February 2020 through Wenjuanxing platform, measuring their knowledge, risk perception, negative emotion, response to official communication, and protective behaviors in relation to COVID-19. A total of 3008 people completed the questionnaire, of which 2845 were valid questionnaires.
Results
On average, 71% of respondents embraced protective behaviors. Those who made no error in the knowledge test (AOR = 1.77, p < 0.001) perceived the high severity of the epidemic (AOR = 1.90, p < 0.001), had high negative emotion (AOR = 1.36, p = 0.005), reported good health (AOR = 1.94, p < 0.001), paid high attention to the governmental media (AOR = 4.16, p < 0.001) and trusted the governmental media (AOR = 1.97, p < 0.001) were more likely to embrace protective behaviors after adjustments for variations in potential confounding factors. Women and older people were also more likely to embrace protective behaviors. No regional or educational differences were found in the adoption of protective behaviors.
Conclusion
The majority of Chinese citizens embraced protective behaviors. Higher levels of protective behaviors are associated with higher knowledge, perceived severity, negative emotion, and attention to and trust in the official governmental media. Official governmental communication is the largest single predictor of protective behaviors.
The current worldwide COVID19 pandemic has required the rapid and drastic adoption of social distancing and protective measures as the leading method for reducing the spread of the disease and death. The purpose of this study is to investigate the factors associated with the adoption of such measures in a large sample of the Brazilian population. We relied on recreancy theory, which argues that confidence in the ability of social institutions and perceived vulnerability to the disease are central factors predicting the adoption of these behaviors. Our results, drawn from 7554 respondents, indicate that self-confidence in the ability to carry out these behaviors, confidence in the ability of social institutions such as the government, hospitals, health workers and the media to cope with the pandemic crisis, and risk perceptions are associated with the adoption of preventive behaviors. Our results expand the recreancy theory and show that beyond the main effects, the effect of perceived vulnerability depends on the values of self-confidence and confidence in social institutions. The theoretical implications of the findings are discussed.
The coronavirus outbreak manifested in Norway in March 2020. It was met with a combination of mandatory changes (closing of public institutions) and recommended changes (hygiene behavior, physical distancing). It has been emphasized that health-protective behavior such as increased hygiene or physical distancing are able to slow the spread of infections and flatten the curve. Drawing on previous health-psychological studies during the outbreak of various pandemics, we investigated psychological and demographic factors predicting the adoption and engagement in health-protective behavior and changes in such behavior, attitudes, and emotions over time. We recruited a non-representative sample of Norwegians (n = 8676) during a 15-day period (March 12–26 2020) at the beginning of the COVID-19 outbreak in Norway. Employing both traditional methods and exploratory machine learning, we replicated earlier findings that engagement in health-protective behavior is associated with specific demographic characteristics. Further, we observed that increased media exposure, perceiving measures as effective, and perceiving the outbreak as serious was positively related to engagement in health-protective behavior. We also found indications that hygiene and physical distancing behaviors were related to somewhat different psychological and demographic factors. Over the sampling period, reported engagement in physical distancing increased, while experienced concern or fear declined. Contrary to previous studies, we found no or only small positive predictions by confidence in authorities, knowledge about the outbreak, and perceived individual risk, while all of those variables were rather high. These findings provide guidance for health communications or interventions targeting the adoption of health-protective behaviors in order to diminish the spread of COVID-19.
Background:
Behavioural science can play a critical role in combatting the effects of an infectious disease outbreak or public health emergency, such as the COVID-19 pandemic. The current paper presents a synthesis of review literature discussing the application of behaviour change theories within an infectious disease and emergency response context, with a view to informing infectious disease modelling, research and public health practice.
Methods:
A scoping review procedure was adopted for the searches. Searches were run on PubMed, PsychInfo and Medline with search terms covering four major categories: behaviour, emergency response (e.g., infectious disease, preparedness, mass emergency), theoretical models, and reviews. Three further top-up reviews was also conducted using Google Scholar. Papers were included if they presented a review of theoretical models as applied to understanding preventative health behaviours in the context of emergency preparedness and response, and/or infectious disease outbreaks.
Results:
Thirteen papers were included in the final synthesis. Across the reviews, several theories of behaviour change were identified as more commonly cited within this context, specifically, Health Belief Model, Theory of Planned Behaviour, and Protection Motivation Theory, with support (although not universal) for their effectiveness in this context. Furthermore, the application of these theories in previous primary research within this context was found to be patchy, and so further work is required to systematically incorporate and test behaviour change models within public health emergency research and interventions.
Conclusion:
Overall, this review identifies a range of more commonly applied theories with broad support for their use within an infectious disease and emergency response context. The Discussion section details several key recommendations to help researchers, practitioners, and infectious disease modellers to incorporate these theories into their work. Specifically, researchers and practitioners should base future research and practice on a systematic application of theories, beginning with those reported herein. Furthermore, infectious disease modellers should consult the theories reported herein to ensure that the full range of relevant constructs (cognitive, emotional and social) are incorporated into their models. In all cases, consultation with behavioural scientists throughout these processes is strongly recommended to ensure the appropriate application of theory.
The COVID-19 pandemic that started in China in December 2019 has not only threatened world public health, but severely impacted almost every facet of life, including behavioural and psychological aspects. In this paper, we focus on the ‘human element’ and propose a mathematical model to investigate the effects on the COVID-19 epidemic of social behavioural changes in response to lockdowns. We consider an SEIR-like epidemic model where the contact and quarantine rates depend on the available information and rumours about the disease status in the community. The model is applied to the case of the COVID-19 epidemic in Italy. We consider the period that stretches between 24 February 2020, when the first bulletin by the Italian Civil Protection was reported and 18 May 2020, when the lockdown restrictions were mostly removed.The role played by the information-related parameters is determined by evaluating how they affect suitable outbreak-severity indicators. We estimate that citizen compliance with mitigation measures played a decisive role in curbing the epidemic curve by preventing a duplication of deaths and about 46% more infections.
Objective: The COVID-19 outbreak means far-reaching changes in the organization of daily lives. Disease-related literacy and factors such as age, gender, or education play a major role in shaping individual practices of protective behavior. This paper investigates different types and frequency of practicing protective behaviors, as well as socio-demographic factors that are associated with such behavioral change.
Methods: Data stem from a cross-sectional survey in Germany. Three thousand seven hundred and sixty-five people were contacted, 3,186 participated in the survey. Information on behavior to lower the risk of becoming infected with COVID-19 was assessed by nine items (answer options yes/no). For each item, logistic regression models were used to estimate odds ratios (OR), using education, sex, and age as main predictors and adjusting for partnership status and household composition.
Results: People with lower educational level were less likely to avoid gatherings (OR = 0.63; 95%CI = 0.48–0.83), adapt their work situation (OR = 0.66; 95%CI = 0.52–0.82), reduce personal contacts and meetings (OR = 0.71; 95%CI = 0.55–0.93), or increase hand hygiene (OR = 0.53; 95%CI = 0.38–0.73). Being female was associated with higher odds of protective behavior for most outcomes. Exceptions were wearing face masks and adapting the own work situation. Associations between respondents' age and individual behavior change were inconsistent and mostly weak.
Conclusion: Disease specific knowledge is essential in order to enable people to judge information on COVID-19. Health education programs aiming at improving COVID-19 knowledge are helpful to build up appropriate practices and reduce the spread of the disease. Strategies are needed to guarantee easy access and better dissemination of high-quality news and fact-checks. Socioeconomic characteristics should be taken into account in the development of infection control measures.
Efforts to change behaviour are critical in minimizing the spread of highly transmissible pandemics such as COVID-19. However, it is unclear whether individuals are aware of disease risk and alter their behaviour early in the pandemic. We investigated risk perception and self-reported engagement in protective behaviours in 1591 United States-based individuals cross-sectionally and longitudinally over the first week of the pandemic. Subjects demonstrated growing awareness of risk and reported engaging in protective behaviours with increasing frequency but underestimated their risk of infection relative to the average person in the country. Social distancing and hand washing were most strongly predicted by the perceived probability of personally being infected. However, a subgroup of individuals perceived low risk and did not engage in these behaviours. Our results highlight the importance of risk perception in early interventions during large-scale pandemics.
Background:
During an evolving outbreak or pandemic, non-pharmaceutical interventions (NPIs) including physical distancing, isolation, and mask use may flatten the peak in communities. However, these strategies rely on community understanding and motivation to engage to ensure appropriate compliance and impact. To support current activities for COVID-19, the objectives of this narrative review was to identify the key determinants impacting on engagement.
Methods:
An integrative narrative literature review focused on NPIs. We aimed to identify published peer-reviewed articles that focused on the general community (excluding healthcare workers), NPIs (including school closure, quarantine, isolation, physical distancing and hygiene behaviours), and factors/characteristics (including social, physical, psychological, capacity, motivation, economic and demographic) that impact on engagement.
Results:
The results revealed that there are a range of demographic, social and psychological factors underpinning engagement with quarantine, school closures, and personal protective behaviours. Aside from the factors impacting on acceptance and compliance, there are several key community concerns about their use that need to be addressed including the potential for economic consequences.
Conclusion:
It is important that we acknowledge that these strategies will have an impact on an individual and the community. By understanding the barriers, we can identify what strategies need to be adopted to motivate individuals and improve community compliance. Using a behavioural framework to plan interventions based on these key barriers, will also ensure countries implement appropriate and targeted responses.
With a large international sample (n=8,317), the present study examined which beliefs and attitudes about COVID-19 predict 1) following government recommendations, 2) taking health precautions (including mask wearing, social distancing, handwashing, and staying at home), and 3) encouraging others to take health precautions. The results demonstrate the importance of believing that taking health precautions will be effective for avoiding COVID-19 and generally prioritizing one’s health. These beliefs continued to be important predictors of health behaviors after controlling for demographic and personality variables. In contrast, we found that perceiving oneself as vulnerable to COVID-19, the perceived severity of catching COVID-19, and trust in government were of relatively little importance. We also found that women were somewhat more likely to engage in these health behaviors than men, but that age was generally unrelated to voluntary compliance behaviors. These findings may suggest avenues and dead ends for behavioral interventions during COVID-19 and beyond.
Introduction
Perceptions of health risks inform decisions about protective behaviors, but coronavirus disease 2019 (COVID-19) was an unfamiliar risk as it began to spread across the U.S. In the initial stage of the epidemic, authors examined perceived risks for COVID-19 infection and infection fatality, and whether these risk perceptions are associated with protective behaviors. They also examined whether findings differed between later versus earlier responders.
Methods
Between March 10 and 31, 2020, investigators conducted a cross-sectional online survey with a nationally representative U.S. sample (N=6,684). Half responded before March 13, 2020 (versus later). Participants assessed their risks of COVID-19 infection and infection fatality (0%–100%), and were transformed into quartiles (1–4). They reported their implementation of protective behaviors like handwashing and social distancing (yes/no). Analyses were conducted in April‒May 2020.
Results
Median perceived risk was 10.00% for COVID-19 infection and 5.00% for infection fatality, but respondents showed large disagreement. An increase of one quartile in perceived infection risk was associated with being 1.45 (95% CI=1.33, 1.58) more likely to report handwashing, with perceived infection fatality risk showing no significant association. When predicting social distancing behaviors such as avoiding crowds, both quartile-based risk perceptions were significant (OR=1.24, 95% CI=1.17, 1.30 for infection and OR=1.19, 95% CI=1.13, 1.26 for infection fatality). Perceived COVID-19 infection risk, protective behaviors, and their relationship increased among later (versus earlier) responders.
Conclusions
Despite disagreements about the risks, people perceiving greater risks were more likely to implement protective behaviors—especially later (versus earlier) in March 2020. These findings have implications for risk communication.
Objectives
To clarify changes in the implementation of personal protective measures among ordinary Japanese citizens from the early phase of the COVID-19 outbreak to the community transmission phase.
Methods
This longitudinal, internet-based survey included 2,141 people (50.8% men; 20-79 years). The baseline and follow-up surveys were conducted from February 25-27, 2020 and April 1-6, 2020, respectively. Participants were asked how often they implemented the 5 personal protective measures recommended by the World Health Organization (hand hygiene, social distancing, avoiding touching the eyes, nose and mouth, respiratory etiquette, and self-isolation) in the baseline and follow-up surveys.
Results
The prevalence of 3 of the 5 personal protective measures significantly improved in the community transmission phase compared to the early phase. Social distancing measures showed significant improvement, from 67.4% to 82.2%. However, the prevalence of avoiding touching the eyes, nose and mouth, which had the lowest prevalence in the early phase, showed no significant improvement (approximately 60%). Multivariate logistic regression analysis revealed, men and persons of low-income households made fewer improvements than women and persons of high-income households.
Conclusions
The prevalence of personal protective measures by ordinary citizens is improving, however there is potential for improvement, especially in regard to avoiding touching eyes, nose and mouth.
Significance
The ongoing pandemic of COVID-19 challenges globalized societies. Scientific and technological cross-fertilization yields broad availability of georeferenced epidemiological data and of modeling tools that aid decisions on emergency management. To this end, spatially explicit models of the COVID-19 epidemic that include e.g. regional individual mobilities, the progression of social distancing, and local capacity of medical infrastructure provide significant information. Data-tailored spatial resolutions that model the disease spread geography can include details of interventions at the proper geographical scale. Based on them, it is possible to quantify the effect of local containment measures (like diachronic spatial maps of averted hospitalizations) and the assessment of the spatial and temporal planning of the needs of emergency measures and medical infrastructure as a major contingency planning aid.
Managing non-communicable diseases requires policy makers to adopt a whole systems perspective that adequately represents the complex causal architecture of human behaviour. Agent-based modelling is a computational method to understand the behaviour of complex systems by simulating the actions of entities within the system, including the way these individuals influence and are influenced by their physical and social environment. The potential benefits of this method have led to several calls for greater use in public health research. We discuss three challenges facing potential modellers: model specification, obtaining required data, and developing good practices. We also present steps to assist researchers to meet these challenges and implement their agent-based model.
Background:
Given the importance of person to person transmission in the spread of infectious diseases, it is critically important to ensure that human behaviour with respect to infection prevention is appropriately represented within infectious disease models. This paper presents a large scale scoping review regarding the incorporation of infection prevention behaviour in infectious disease models. The outcomes of this review are contextualised within the psychological literature concerning health behaviour and behaviour change, resulting in a series of key recommendations for the incorporation of human behaviour in future infectious disease models.
Methods:
The search strategy focused on terms relating to behaviour, infectious disease and mathematical modelling. The selection criteria were developed iteratively to focus on original research articles that present an infectious disease model with human-human spread, in which individuals' self-protective health behaviour varied endogenously within the model. Data extracted included: the behaviour that is modelled; how this behaviour is modelled; any theoretical background for the modelling of behaviour, and; any behavioural data used to parameterise the models.
Results:
Forty-two papers from an initial total of 2987 were retained for inclusion in the final review. All of these papers were published between 2002 and 2015. Many of the included papers employed a multiple, linked models to incorporate infection prevention behaviour. Both cognitive constructs (e.g., perceived risk) and, to a lesser extent, social constructs (e.g., social norms) were identified in the included papers. However, only five papers made explicit reference to psychological health behaviour change theories. Finally, just under half of the included papers incorporated behavioural data in their modelling.
Conclusions:
By contextualising the review outcomes within the psychological literature on health behaviour and behaviour change, three key recommendations for future behavioural modelling are made. First, modellers should consult with the psychological literature on health behaviour/ behaviour change when developing new models. Second, modellers interested in exploring the relationship between behaviour and disease spread should draw on social psychological literature to increase the complexity of the social world represented within infectious disease models. Finally, greater use of context-specific behavioural data (e.g., survey data, observational data) is recommended to parameterise models.
Pattern oriented modelling (POM) is an approach to calibration or validation that assesses a model using multiple weak patterns. We extend the concept of POM, using dominance to objectively identify the best parameter candidates. The TELL ME agent-based model is used to demonstrate the approach. This model simulates personal decisions to adopt protective behaviour during an influenza epidemic. The model fit is assessed by the size and timing of maximum behaviour adoption, aswell as the more usual criterion of minimising mean squared error between actual and estimated behaviour. The rigorous approach to calibration supported explicit trading off between these criteria, and ultimately demonstrated that there were significant flaws in the model structure.
We review behavioural change models (BCMs) for infectious disease transmission in humans. Following the Cochrane collaboration guidelines and the PRISMA statement, our systematic search and selection yielded 178 papers covering the period 2010–2015. We observe an increasing trend in published BCMs, frequently coupled to (re)emergence events, and propose a categorization by distinguishing how information translates into preventive actions. Behaviour is usually captured by introducing information as a dynamic parameter (76/178) or by introducing an economic objective function, either with (26/178) or without (37/178) imitation. Approaches using information thresholds (29/178) and exogenous behaviour formation (16/178) are also popular. We further classify according to disease, prevention measure, transmission model (with 81/178 population, 6/178 metapopulation and 91/178 individual-level models) and the way prevention impacts transmission. We highlight the minority (15%) of studies that use any real-life data for parametrization or validation and note that BCMs increasingly use social media data and generally incorporate multiple sources of information (16/178), multiple types of information (17/178) or both (9/178). We conclude that individual-level models are increasingly used and useful to model behaviour changes. Despite recent advancements, we remain concerned that most models are purely theoretical and lack representative data and a validation process.
Setting the free parameters of classifiers to different values can have a profound impact on their performance. For some methods, specialized tuning algorithms have been developed. These approaches mostly tune parameters according to a single criterion, such as the cross-validation error. However, it is sometimes desirable to obtain parameter values that optimize several concurrent - often conicting - criteria. The TunePareto package provides a general and highly customizable framework to select optimal parameters for classifiers according to multiple objectives. Several strategies for sampling and optimizing parameters are supplied. The algorithm determines a set of Pareto-optimal parameter configurations and leaves the ultimate decision on the weighting of objectives to the researcher. Decision support is provided by novel visualization techniques.
Research dealing with various aspects of* the theory of planned behavior (Ajzen, 1985,
1987) is reviewed, and some unresolved issues are discussed. In broad terms, the theory is
found to be well supported by empirical evidence. Intentions to perform behaviors of different
kinds can be predicted with high accuracy from attitudes toward the behavior, subjective
norms, and perceived behavioral control; and these intentions, together with perceptions of
behavioral control, account for considerable variance in actual behavior. Attitudes, subjective
norms, and perceived behavioral control are shown to be related to appropriate sets of salient
behavioral, normative, and control beliefs about the behavior, but the exact nature of these
relations is still uncertain. Expectancy value formulations are found to be only partly
successful in dealing with these relations. Optimal rescaling of expectancy and value
measures is offered as a means of dealing with measurement limitations. Finally, inclusion of
past behavior in the prediction equation is shown to provide a means of testing the theory*s
sufficiency, another issue that remains unresolved. The limited available evidence concerning
this question shows that the theory is predicting behavior quite well in comparison to the
ceiling imposed by behavioral reliability.
Traditionally, the spread of infectious diseases in human populations has been modelled with static parameters. These parameters, however, can change when individuals change their behaviour. If these changes are themselves influenced by the disease dynamics, there is scope for mechanistic models of behaviour to improve our understanding of this interaction. Here, we present challenges in modelling changes in behaviour relating to disease dynamics, specifically: how to incorporate behavioural changes in models of infectious disease dynamics, how to inform measurement of relevant behaviour to parameterise such models, and how to determine the impact of behavioural changes on observed disease dynamics.
Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.
It has been argued that spatially explicit population models (SEPMs) cannot provide reliable guidance for conservation biology because of the difficulty of obtaining direct estimates for their demographic and dispersal parameters and because of error propagation. We argue that appropriate model calibration procedures can access additional sources of information, compensating the lack of direct parameter estimates. Our objective is to show how model calibration using population-level data can facilitate the construction of SEPMs that produce reliable predictions for conservation even when direct parameter estimates are inadequate. We constructed a spatially explicit and individual-based population model for the dynamics of brown bears (Ursus arctos) after a reintroduction program in Austria. To calibrate the model we developed a procedure that compared the simulated population dynamics with distinct features of the known population dynamics (=patterns). This procedure detected model parameterizations that did not reproduce the known dynamics. Global sensitivity analysis of the uncalibrated model revealed high uncertainty in most model predictions due to large parameter uncertainties (coefficients of variation CV 0.8). However, the calibrated model yielded predictions with considerably reduced uncertainty (CV 0.2). A pattern or a combination of various patterns that embed information on the entire model dynamics can reduce the uncertainty in model predictions, and the application of different patterns with high information content yields the same model predictions. In contrast, a pattern that does not embed information on the entire population dynamics (e.g., bear observations taken from sub-areas of the study area) does not reduce uncertainty in model predictions. Because population-level data for defining (multiple) patterns are often available, our approach could be applied widely.
The effects of fear appeals on persuasion were investigated in a factorial experiment that was designed to test a combined model of protection motivation theory and self-efficacy theory. As predicted, the probability of a threat's occurrence and the effectiveness of a coping response both had positive main effects on intentions to adopt a recommended preventive health behavior. More importantly, the findings provided support for self-efficacy expectancy as a fourth component of protection motivation theory: Self-efficacy had a direct influence on intentions and interacted with two other variables of protection motivation theory. The interaction effect was interpreted in terms of two new decision-making strategies that people use when confronted with a fear appeal: a precaution strategy and a hyperdefensiveness strategy. In addition, the results replicated previous findings on the relationship between self-efficacy expectancy and outcome expectancy. A model incorporating protection motivation theory and self-efficacy theory is presented as a possible general model of attitude change.
It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transmission model. We formalize the health belief model and demonstrate its application in modelling the prevalence of facemask use observed over the course of the 2003 Hong Kong SARS epidemic, a well-documented example of behaviour change in response to a disease outbreak.
Human behaviour plays an important role in the spread of infectious diseases, and understanding the influence of behaviour on the spread of diseases can be key to improving control efforts. While behavioural responses to the spread of a disease have often been reported anecdotally, there has been relatively little systematic investigation into how behavioural changes can affect disease dynamics. Mathematical models for the spread of infectious diseases are an important tool for investigating and quantifying such effects, not least because the spread of a disease among humans is not amenable to direct experimental study. Here, we review recent efforts to incorporate human behaviour into disease models, and propose that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, and according to the assumed effects of such behaviour. We highlight recent advances as well as gaps in our understanding of the interplay between infectious disease dynamics and human behaviour, and suggest what kind of data taking efforts would be helpful in filling these gaps.
The relationships between risk perception and related behavior form a fundamental theme in risk analysis. Despite increasing attentions on the temporal dimension of risk perception and behavior in recent literature, the dynamic relationships between these two constructs remain understudied. Infectious disease outbreaks, such as the Coronavirus Disease 2019 (COVID-19) pandemic, provide a key setting for analyzing evolving perceptions of and responses to natural or human-induced hazards. The main objectives of this research are: (1) to assess temporal changes in cognitive and affective dimensions of perceived COVID-19 risk as well as related protective behavior; and (2) to explore the dynamic relationship between COVID-19 risk perception and behavioral response. Timely data on changing risk perception and behavior related to the COVID-19 outbreak were collected through a series of online surveys from four major cities (Seattle, Los Angeles, Chicago, and New York City; N = 736) and the central Midwest region of the United States (N = 1240) during March–August 2020. The analysis revealed that: (1) the cognitive and affective dimensions of perceived COVID-19 risk and preventive behavior all changed over time; (2) there were both within- and across-time correlations between COVID-19 risk perception indicators and preventive actions; and (3) preventive actions showed varied feedback effects on individual aspects of perceived COVID-19 risk over time. Findings from this research support and expand major conceptual approaches to changing relationships between risk perception and behavior, particularly the risk reappraisal hypothesis. The study also has useful implications for health risk management and future research directions.
In the absence of a vaccine, governments have focused on behaviour change (e.g. social distancing and enhanced hygiene procedures) to tackle the COVID-19 pandemic. Existing research on the predictors of compliance with pandemic measures has often produced discrepant results. One explanation for this may be that the determinants of compliance are context specific. Understanding whether this is the case is important for designing public health messaging and for evaluating the generalisability of existing research. We used data from the UCL COVID-19 Social Study; a large weekly panel of UK adults from first five months of lockdown in the UK (n = 22,625). We tested whether the extent to which demographic, socio-economic position, personality traits, social and pro-social motivations, and the living environment predict compliance changed across the pandemic using multilevel regression modelling. Low compliance was strongly related to younger age and also to risk attitudes, empathic concern, and high income, among other factors. The size of some of these associations was larger in later months when less stringent lockdown and household mixing measures were in place. The results showed that compliance was lower and fell faster across some groups, suggesting the importance that public health communications adopt a plurality of messages to maximize broad adherence.
When planning interventions to limit the spread of Covid-19, the current state of knowledge about the disease and specific characteristics of the population need to be considered. Simulations can facilitate policy making as they take prevailing circumstances into account. Moreover, they allow for the investigation of the potential effects of different interventions using an artificial population. Agent-based Social Simulation (ABSS) is argued to be particularly useful as it can capture the behavior of and interactions between individuals. We performed a systematic literature review and identified 126 articles that describe ABSS of Covid-19 transmission processes. Our review showed that ABSS is widely used for investigating the spread of Covid-19. Existing models are very heterogeneous with respect to their purpose, the number of simulated individuals, and the modeled geographical region, as well as how they model transmission dynamics, disease states, human behavior, and interventions. To this end, a discrepancy can be identified between the needs of policy makers and what is implemented by the simulation models. This also includes how thoroughly the models consider and represent the real world, e.g. in terms of factors that affect the transmission probability or how humans make decisions. Shortcomings were also identified in the transparency of the presented models, e.g. in terms of documentation or availability, as well as in their validation, which might limit their suitability for supporting decision-making processes. We discuss how these issues can be mitigated to further establish ABSS as a powerful tool for crisis management.
Most SARS-CoV-2 infections are undocumented. French health-care records and modelling were used to assess the rate of documentation of COVID-19 cases. The findings highlight the need for improved identification of infections. Disease documentation assessed using medical records and modelling.
Objectives
We aim to identify the factors associated with support and compliance with general quarantine and with the acceptability and potential use of a contact-tracing mobile phone application among French respondents.
Study design
We conducted a cross-sectional study between April 16th and May 7th 2020 using online questionnaires.
Methods
The sample was reweighted to be representative of the French population by age and sex, region and education level. Ordered logistic, logistic and negative binomial regressions were used to estimate the factors associated with quarantine support, with the opinion on quarantine extension, with the number and type of trips outside the quarantine home and with the acceptability and potential use of a contact-tracing application.
Results
After reweighting, full data for regression analyses were available for 1849 respondents. Attitudes and opinions regarding quarantine are correlated with the perceived COVID-19 threat, the perceived benefits of quarantine, trust in the government, well-being during quarantine and risk preferences. Trust in the government, perceived individual health consequences in case of COVID-19 infection and time preferences are associated with the willingness to use a contact-tracing application.
Conclusions
Our analysis indicates that prevention campaigns that stress the individual risk in case of infection or the benefits of quarantine could foster compliance to quarantine protocols. Remote psychological support might also promote quarantine adherence among individuals most distressed by the quarantine. Moreover, public communications should focus on restoring trust among the population as trust is strongly correlated with the willingness to use a contact-tracing application.
Previous studies have revealed medical, democratic, and political factors altering responses to unexpected infectious diseases. However, few studies have attempted to explore the factors affecting disease infection from a social perspective. Here, we argue that trust, which plays an important role in shaping people’ s risk perception toward hazards, can also affect risk perception toward infections from a social perspective. Drawing on the indication that risk perception of diseases helps prevent people from being infected by promoting responsible behaviors, it can be further asserted that trust may alter the infection rate of diseases as a result of risk perception toward infectious diseases. This is an essential point for preventing the spread of infectious diseases and should be demonstrated. To empirically test this prediction, this study uses the COVID-19 outbreak in China as an example and applies an original dataset combining real-time big data, official data, and social survey data from 317 cities in 31 Chinese provinces to demonstrate whether trust influences the infection rate of diseases. Multilevel regression analyses reveal three main results: (1) trust in local government and media helps to reduce the infection rate of diseases; (2) generalized trust promotes a higher rather than lower infection rate; and (3) the effects of different types of trust are either completely or partly mediated by risk perception toward diseases. The theoretical and practical implications of this study provide suggestions for improving the public health system in response to possible infectious diseases.
Objectives
Data relating to the novel coronavirus disease (COVID-19) in the Middle East remains sparse. This study examines the public’s perceptions of the pandemic, assesses the extent to which participants have adhered to a range of recommended health-protective behaviours to prevent infection and spread of the disease, and evaluates whether anxiety about COVID-19 or perceptions related to the pandemic are associated with greater adherence to these behaviours.
Study Design
A cross-sectional, survey-based design was employed. Data were collected using an electronic survey distributed to students, staff, and faculty at universities in the three major cities of the United Arab Emirates (UAE), Abu Dhabi, Al Ain, and Dubai, between the 23rd and 31st April 2020 when government-mandated restrictions were at their most stringent. A total of 634 participants were included in the analysis.
Methods
Participants reported whether they had adhered to health-protective behaviours such as spatial distancing, increased hygiene and disinfection, and diminished time spent outside their homes. They also reported the perceived efficacy of a range of behaviours aimed at reducing risk for contracting COVID-19. Data relating to perception of risk, negative consequences of contracting the disease, perceived longevity of the illness, and perceptions of the accuracy of the information read about COVID-19 were collected. Anxiety related to COVID-19 was also assessed as well as a range of demographic variables. Binary logistic regressions were used to examine whether the demographic variables, perceived efficacy ratings, and the perception variables were associated with overall adherence to the health-protective behaviours.
Results
44.8% of the sample reported adherence to all the examined health-protective behaviours. Participants who were employed, those with some or completed post-secondary education, and those with a chronic illness diagnosis were more likely to adhere to the precautionary behaviours. The perception of personal risk of infection (OR 0.83, 95% CI 0.71 - 0.98), perception of substantial life consequences of becoming infected (OR 0.87, 95% CI 0.75 - 0.10), and the perception that the public health information was clear (OR 0.69, 95% CI 0.57 - 0.83) were all positively related with behavioural adherence. The health-protective behaviours were all perceived as being highly efficacious in combating infection and these efficacy ratings were also positively associated with greater behavioural adherence (OR 0.41 to 0.77). Having read the official government public health information was related to greater behavioural adherence (OR 0.37, 95% CI 0.23 - 0.61).
Conclusions
Dissemination of reliable public health information during a public health crisis is essential. This study’s results highlight the importance of providing the public with information that is clear and consistent and, moreover, emphasises the efficacy of the recommended behaviours as this is likely to improve adherence. When individuals perceive themselves to be at personal risk and are aware of the severity of the consequences posed by the illness, they are more likely to adopt caution. However, in this sample, the trustworthiness of the information portrayed in the media and the perceived duration of the pandemic – whether this would resolve soon or persist well into the future – did not impact adherence to precautionary behaviour.
Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.
Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals' protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.
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