Infection prevention behaviour and infectious disease modelling: a review of the literature and recommendations for the future

Examining the effects of voluntary avoidance behaviour and policy-mediated behaviour change on the dynamics of SARS-CoV-2: A mathematical model

Background

Throughout the SARS-CoV-2 pandemic, policymakers have had to navigate between recommending voluntary behaviour change and policy-driven behaviour change to mitigate the impact of the virus. While individuals will voluntarily engage in self-protective behaviour when there is an increasing infectious disease risk, the extent to which this occurs and its impact on an epidemic is not known.

Methods

This paper describes a deterministic disease transmission model exploring the impact of individual avoidance behaviour and policy-mediated avoidance behaviour on epidemic outcomes during the second wave of SARS-CoV-2 infections in Ontario, Canada (September 1, 2020 to February 28, 2021). The model incorporates an information feedback function based on empirically derived behaviour data describing the degree to which avoidance behaviour changed in response to the number of new daily cases COVID-19.

Results

Voluntary avoidance behaviour alone was estimated to reduce the final attack rate by 23.1%, the total number of hospitalizations by 26.2%, and cumulative deaths by 27.5% over 6 months compared to a counterfactual scenario in which there were no interventions or avoidance behaviour. A provincial shutdown order issued on December 26, 2020 was estimated to reduce the final attack rate by 66.7%, the total number of hospitalizations by 66.8%, and the total number of deaths by 67.2% compared to the counterfactual scenario.

Conclusion

Given the dynamics of SARS-CoV-2 in a pre-vaccine era, individual avoidance behaviour in the absence of government action would have resulted in a moderate reduction in disease however, it would not have been sufficient to entirely mitigate transmission and the associated risk to the population in Ontario. Government action during the second wave of the COVID-19 pandemic in Ontario reduced infections, protected hospital capacity, and saved lives.

Determinants of intended prevention behaviour against mosquitoes and mosquito-borne viruses in the Netherlands and Spain using the MosquitoWise survey: cross-sectional study

BACKGROUND

Recently, Europe has seen an emergence of mosquito-borne viruses (MBVs). Understanding citizens' perceptions of and behaviours towards mosquitoes and MBVs is crucial to reduce disease risk. We investigated and compared perceptions, knowledge, and determinants of citizens' behavioural intentions related to mosquitoes and MBVs in the Netherlands and Spain, to help improve public health interventions.

METHODS

Using the validated MosquitoWise survey, data was collected through participant panels in Spain (N = 475) and the Netherlands (N = 438). Health Belief Model scores measuring behavioural intent, knowledge, and information scores were calculated. Confidence Interval-Based Estimation of Relevance was used, together with potential for change indexes, to identify promising determinants for improving prevention measure use.

RESULTS

Spanish participants' responses showed slightly higher intent to use prevention measures compared to those of Dutch participants (29.1 and 28.2, respectively, p 0.03). Most participants in Spain (92.2%) and the Netherlands (91.8%) indicated they used at least one prevention measure, but differences were observed in which types they used. More Spanish participants indicated to have received information on mosquitoes and MBVs compared to Dutch participants. Spanish participants preferred health professional information sources, while Dutch participants favoured government websites. Determinants for intent to use prevention measures included "Knowledge", "Reminders to Use Prevention Measures", and "Information" in the Netherlands and Spain. Determinants for repellent use included "Perceived Benefits" and "Cues to Action", with "Perceived Benefits" having a high potential for behavioural change in both countries. "Self-Efficacy" and "Knowledge" were determinants in both countries for breeding site removal.

CONCLUSION

This study found differences in knowledge between the Netherlands and Spain but similarities in determinants for intent to use prevention measures, intent to use repellents and intent to remove mosquito breeding sites. Identified determinants can be the focus for future public health interventions to reduce MBV risks.

A behaviour and disease transmission model: incorporating the Health Belief Model for human behaviour into a simple transmission model

The health and economic impacts of infectious diseases such as COVID-19 affect all levels of a community from the individual to the governing bodies. However, the spread of an infectious disease is intricately linked to the behaviour of the people within a community since crowd behaviour affects individual human behaviour, while human behaviour affects infection spread, and infection spread affects human behaviour. Capturing these feedback loops of behaviour and infection is a well-known challenge in infectious disease modelling. Here, we investigate the interface of behavioural science theory and infectious disease modelling to explore behaviour and disease (BaD) transmission models. Specifically, we incorporate a visible protective behaviour into the susceptible-infectious-recovered-susceptible (SIRS) transmission model using the socio-psychological Health Belief Model to motivate behavioural uptake and abandonment. We characterize the mathematical thresholds for BaD emergence in the BaD SIRS model and the feasible steady states. We also explore, under different infectious disease scenarios, the effects of a fully protective behaviour on long-term disease prevalence in a community, and describe how BaD modelling can investigate non-pharmaceutical interventions that target-specific components of the Health Belief Model. This transdisciplinary BaD modelling approach may reduce the health and economic impacts of future epidemics.

Incorporating endogenous human behavior in models of COVID-19 transmission: A systematic scoping review

Background

During the COVID-19 pandemic there was a plethora of dynamical forecasting models created, but their ability to effectively describe future trajectories of disease was mixed. A major challenge in evaluating future case trends was forecasting the behavior of individuals. When behavior was incorporated into models, it was primarily incorporated exogenously (e.g., fitting to cellphone mobility data). Fewer models incorporated behavior endogenously (e.g., dynamically changing a model parameter throughout the simulation).

Methods

This review aimed to qualitatively characterize models that included an adaptive (endogenous) behavioral element in the context of COVID-19 transmission. We categorized studies into three approaches: 1) feedback loops, 2) game theory/utility theory, and 3) information/opinion spread.

Findings

Of the 92 included studies, 72% employed a feedback loop, 27% used game/utility theory, and 9% used a model if information/opinion spread. Among all studies, 89% used a compartmental model alone or in combination with other model types. Similarly, 15% used a network model, 11% used an agent-based model, 7% used a system dynamics model, and 1% used a Markov chain model. Descriptors of behavior change included mask-wearing, social distancing, vaccination, and others. Sixty-eight percent of studies calibrated their model to observed data and 25% compared simulated forecasts to observed data. Forty-one percent of studies compared versions of their model with and without endogenous behavior. Models with endogenous behavior tended to show a smaller and delayed initial peak with subsequent periodic waves.

Interpretation

While many COVID-19 models incorporated behavior exogenously, these approaches may fail to capture future adaptations in human behavior, resulting in under- or overestimates of disease burden. By incorporating behavior endogenously, the next generation of infectious disease models could more effectively predict outcomes so that decision makers can better prepare for and respond to epidemics.

Funding

This study was funded in-part by Centers for Disease Control and Prevention (CDC) MInD-Healthcare Program (1U01CK000536), the National Science Foundation (NSF) Modeling Dynamic Disease-Behavior Feedbacks for Improved Epidemic Prediction and Response grant (2229996), and the NSF PIPP Phase I: Evaluating the Effectiveness of Messaging and Modeling during Pandemics grant (2200256).

Autonomous and policy-induced behavior change during the COVID-19 pandemic: Towards understanding and modeling the interplay of behavioral adaptation

Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a "moving target". Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately.

Parameter estimation in behavioral epidemic models with endogenous societal risk-response

Behavioral epidemic models incorporating endogenous societal risk-response, where changes in risk perceptions prompt adjustments in contact rates, are crucial for predicting pandemic trajectories. Accurate parameter estimation in these models is vital for validation and precise projections. However, few studies have examined the problem of identifiability in models where disease and behavior parameters must be jointly estimated. To address this gap, we conduct simulation experiments to assess the effect on parameter estimation accuracy of a) delayed risk response, b) neglecting behavioral response in model structure, and c) integrating disease and public behavior data. Our findings reveal systematic biases in estimating behavior parameters even with comprehensive and accurate disease data and a well-structured simulation model when data are limited to the first wave. This is due to the significant delay between evolving risks and societal reactions, corresponding to the duration of a pandemic wave. Moreover, we demonstrate that conventional SEIR models, which disregard behavioral changes, may fit well in the early stages of a pandemic but exhibit significant errors after the initial peak. Furthermore, early on, relatively small data samples of public behavior, such as mobility, can significantly improve estimation accuracy. However, the marginal benefits decline as the pandemic progresses. These results highlight the challenges associated with the joint estimation of disease and behavior parameters in a behavioral epidemic model.

Pharmaceutical and non-pharmaceutical interventions for controlling the COVID-19 pandemic

Disease spread can be affected by pharmaceutical interventions (such as vaccination) and non-pharmaceutical interventions (such as physical distancing, mask-wearing and contact tracing). Understanding the relationship between disease dynamics and human behaviour is a significant factor to controlling infections. In this work, we propose a compartmental epidemiological model for studying how the infection dynamics of COVID-19 evolves for people with different levels of social distancing, natural immunity and vaccine-induced immunity. Our model recreates the transmission dynamics of COVID-19 in Ontario up to December 2021. Our results indicate that people change their behaviour based on the disease dynamics and mitigation measures. Specifically, they adopt more protective behaviour when mandated social distancing measures are in effect, typically concurrent with a high number of infections. They reduce protective behaviour when vaccination coverage is high or when mandated contact reduction measures are relaxed, typically concurrent with a reduction of infections. We demonstrate that waning of infection and vaccine-induced immunity are important for reproducing disease transmission in autumn 2021.

Habits and Reflective Processes in COVID-19 Transmission-reducing Behaviors: Examining Theoretical Predictions in a Representative Sample of the Population of Scotland

BACKGROUND

Based on theory, COVID-19 transmission-reducing behaviors (TRBs) should become habitual because of their frequent performance. Habits have been hypothesized to develop through reflective processes and, to act in conjunction with them.

PURPOSE

We investigated the existence, development, and consequences of TRB habits, for physical distancing, handwashing, and wearing face coverings.

METHODS

A representative sample of the Scottish population (N = 1,003) was interviewed by a commercial polling company in August-October 2020 and half were re-interviewed later. Measures included adherence, habit, personal routine tendency, reflective processes, and action control for three TRBs. Data were analyzed using general linear modeling, regression, and mediation analyses.

RESULTS

Handwashing was most habitual; only face covering became more habitual over time. Routine tendencies predicted TRB habits, and adherence to handwashing and physical distancing. Those reporting greater habits reported better adherence, for physical distancing and handwashing, and this remained true after controlling for previous adherence. Reflective and habit processes independently predicted adherence for physical distancing and handwashing; only reflective processes were independently predictive for face covering. The relationship between planning and forgetting and adherence was partly direct, and partly mediated by habit.

CONCLUSIONS

The results confirm hypotheses from habit theory including the role of repetition and of personal routine tendency in developing habits. They are consistent with dual processing theory in finding that both reflective and habit processes predict adherence to TRBs. Action planning partly mediated the relation between reflective processes and adherence. The COVID-19 pandemic has enabled the testing and confirmation of several theoretical hypotheses about habit processes in the enactment of TRBs.

Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico

Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)-that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model.

Why we should care about moral foundations when preparing for the next pandemic: Insights from Canada, the UK and the US

Health behaviors that do not effectively prevent disease can negatively impact psychological wellbeing and potentially drain motivations to engage in more effective behavior, potentially creating higher health risk. Despite this, studies linking "moral foundations" (i.e., concerns about harm, fairness, purity, authority, ingroup, and/or liberty) to health behaviors have generally been limited to a narrow range of behaviors, specifically effective ones. We therefore explored the degree to which moral foundations predicted a wider range of not only effective but ineffective (overreactive) preventative behaviors during the COVID-19 pandemic. In Study 1, participants from Canada, the United Kingdom, and the United States reported their engagement in these preventative behaviors and completed a COVID-specific adaptation of the Moral Foundations Questionnaire during the pandemic peak. While differences occurred across countries, authority considerations consistently predicted increased engagement in both effective preventative behaviors but also ineffective overreactions, even when controlling for political ideology. By contrast, purity and liberty considerations reduced intentions to engage in effective behaviors like vaccination but had no effect on ineffective behaviors. Study 2 revealed that the influence of moral foundations on U.S participants' behavior remained stable 5-months later, after the pandemic peak. These findings demonstrate that the impact of moral foundations on preventative behaviors is similar across a range of western democracies, and that recommendations by authorities can have unexpected consequences in terms of promoting ineffective-and potentially damaging-overreactive behaviors. The findings underscore the importance of moral concerns for the design of health interventions that selectively promote effective preventative behavior.

The impact of threshold decision mechanisms of collective behavior on disease spread

Humans are a hyper-social species, which greatly impacts the spread of infectious diseases. How do social dynamics impact epidemiology and what are the implications for public health policy? Here, we develop a model of disease transmission that incorporates social dynamics and a behavior that reduces the spread of disease, a voluntary nonpharmaceutical intervention (NPI). We use a "tipping-point" dynamic, previously used in the sociological literature, where individuals adopt a behavior given a sufficient prevalence of the behavior in the population. The thresholds at which individuals adopt the NPI behavior are modulated by the perceived risk of infection, i.e., the disease prevalence and transmission rate, costs to adopt the NPI behavior, and the behavior of others. Social conformity creates a type of "stickiness" whereby individuals are resistant to changing their behavior due to the population's inertia. In this model, we observe a nonmonotonicity in the attack rate as a function of various biological and social parameters such as the transmission rate, efficacy of the NPI, costs of the NPI, weight of social consequences of shirking the social norm, and the degree of heterogeneity in the population. We also observe that the attack rate can be highly sensitive to these parameters due to abrupt shifts in the collective behavior of the population. These results highlight the complex interplay between the dynamics of epidemics and norm-driven collective behaviors.

Social network and household exposure explain the use of malaria prevention measures in rural communities of Meghalaya, India

Malaria remains a global concern despite substantial reduction in incidence over the past twenty years. Public health interventions to increase the uptake of preventive measures have contributed to this decline but their impact has not been uniform. To date, we know little about what determines the use of preventive measures in rural, hard-to-reach populations, which are crucial contexts for malaria eradication. We collected detailed interview data on the use of malaria preventive measures, health-related discussion networks, individual characteristics, and household composition in ten tribal, malaria-endemic villages in Meghalaya, India in 2020-2021 (n=1,530). Employing standard and network statistical models, we found that social network and household exposure were consistently positively associated with preventive measure use across villages. Network and household exposure were also the most important factors explaining behaviour, outweighing individual characteristics, opinion leaders, and network size. These results suggest that real-life data on social networks and household composition should be considered in studies of health-behaviour change.

Testing a simple and frugal model of health protective behaviour in epidemic times

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.

Social divisions and risk perception drive divergent epidemics and large later waves

During infectious disease outbreaks, individuals may adopt protective measures like vaccination and physical distancing in response to awareness of disease burden. Prior work showed how feedbacks between epidemic intensity and awareness-based behaviour shape disease dynamics. These models often overlook social divisions, where population subgroups may be disproportionately impacted by a disease and more responsive to the effects of disease within their group. We develop a compartmental model of disease transmission and awareness-based protective behaviour in a population split into two groups to explore the impacts of awareness separation (relatively greater in- vs. out-group awareness of epidemic severity) and mixing separation (relatively greater in- vs. out-group contact rates). Using simulations, we show that groups that are more separated in awareness have smaller differences in mortality. Fatigue (i.e. abandonment of protective measures over time) can drive additional infection waves that can even exceed the size of the initial wave, particularly if uniform awareness drives early protection in one group, leaving that group largely susceptible to future infection. Counterintuitively, vaccine or infection-acquired immunity that is more protective against transmission and mortality may indirectly lead to more infections by reducing perceived risk of infection and therefore vaccine uptake. Awareness-based protective behaviour, including awareness separation, can fundamentally alter disease dynamics. Social media summary: Depending on group division, behaviour based on perceived risk can change epidemic dynamics & produce large later waves.

A case-crossover study of the effect of vaccination on SARS-CoV-2 transmission relevant behaviours during a period of national lockdown in England and Wales

BACKGROUND

Studies of COVID-19 vaccine effectiveness show increases in COVID-19 cases within 14 days of a first dose, potentially reflecting post-vaccination behaviour changes associated with SARS-CoV-2 transmission before vaccine protection. However, direct evidence for a relationship between vaccination and behaviour is lacking. We aimed to examine the association between vaccination status and self-reported non-household contacts and non-essential activities during a national lockdown in England and Wales.

METHODS

Participants (n = 1154) who had received the first dose of a COVID-19 vaccine reported non-household contacts and non-essential activities from February to March 2021 in monthly surveys during a national lockdown in England and Wales. We used a case-crossover study design and conditional logistic regression to examine the association between vaccination status (pre-vaccination vs 14 days post-vaccination) and self-reported contacts and activities within individuals. Stratified subgroup analyses examined potential effect heterogeneity by sociodemographic characteristics such as sex, household income or age group.

RESULTS

457/1154 (39.60 %) participants reported non-household contacts post-vaccination compared with 371/1154 (32.15 %) participants pre-vaccination. 100/1154 (8.67 %) participants reported use of non-essential shops or services post-vaccination compared with 74/1154 (6.41 %) participants pre-vaccination. Post-vaccination status was associated with increased odds of reporting non-household contacts (OR 1.65, 95 % CI 1.31-2.06, p < 0.001) and use of non-essential shops or services (OR 1.50, 95 % CI 1.03-2.17, p = 0.032). This effect varied between men and women and different age groups.

CONCLUSION

Participants had higher odds of reporting non-household contacts and use of non-essential shops or services within 14 days of their first COVID-19 vaccine compared to pre-vaccination. Public health emphasis on maintaining protective behaviours during this post-vaccination time period when individuals have yet to develop full protection from vaccination could reduce risk of SARS-CoV-2 infection.

The effects of information framing on self-protective behavior: Evidence from the COVID-19 vaccine uptake

Objectives

The Healthy China 2030 strategy outlines the government's plans for healthcare reform, emphasizing the need for increased awareness about infectious diseases to prevent and fight future infections. Information campaigns can be used as a medium to raise awareness and encourage citizens' willingness to protect themselves against diseases, such as COVID-19. Extant studies have found that individual health behavior decision-making can be changed under different information frames. However, limited evidence is available about emerging infectious diseases. Based on the Prospect Theory and Theory of Planned Behavior, the impact of information frames on self-protective behavior-vaccination against COVID-19 is investigated in this study.

Methods

A 2(gain/loss frame)*2(factual/emotional frame) intergroup experimental design was designed to explore the effects of different information frames. 228 valid participants in China were recruited and the experiment was performed online.

Results

First, the gain frame was more effective in promoting public self-protection behavior than the loss frame under information frame intervention. Compared with the factual frame, the emotional frame is more effective in reducing individual risk perception. Second, perceptual behavior control has masking effects on self-protection behavior under the influence of the gain/loss frame. Third, age, subjective norms, attitudes, and the gain frame, have predictive effects on self-protection behavior.

Conclusions

This study provides empirical evidence on the impact of information framing interventions on public self-protection behavior during the COVID-19 pandemic and provides important practical implications for public administrators and media practitioners.

Influence of Lived Experiences on Public Responses to Future Diseases via (De)Sensitization of Concern

OBJECTIVES

Public responses to a future novel disease might be influenced by a subset of individuals who are either sensitized or desensitized to concern-generating processes through their lived experiences during the coronavirus disease 2019 (COVID-19) pandemic. Such influences may be critical for shaping public health messaging during the next emerging threat.

METHODS

This study explored the potential outcomes of the influence of lived experiences by using a dynamic multiplex network model to simulate a COVID-19 outbreak in a population of 2000 individuals, connected by means of disease and communication layers. Then a new disease was introduced, and a subset of individuals (50% or 100% of hospitalized during the COVID-19 outbreak) was assumed to be either sensitized or desensitized to concern-generating processes relative to the general population, which alters their adoption of non-pharmaceutical interventions (social distancing).

RESULTS

Altered perceptions and behaviors from lived experiences with COVID-19 did not necessarily result in a strong mitigating effect for the novel outbreak. When public disease response is already strong or sensitization is assumed to be a robust effect, then a sensitized subset may enhance public mitigation of an outbreak through social distancing.

CONCLUSIONS

In preparing for future outbreaks, assuming an experienced and disease-aware public may compromise effective design of effective public health messaging and mitigative action.

Infectious Disease Modeling with Socio-Viral Behavioral Aspects-Lessons Learned from the Spread of SARS-CoV-2 in a University

When it comes to understanding the spread of COVID-19, recent studies have shown that pathogens can be transmitted in two ways: direct contact and airborne pathogens. While the former is strongly related to the distancing behavior of people in society, the latter are associated with the length of the period in which the airborne pathogens remain active. Considering those facts, we constructed a compartmental model with a time-dependent transmission rate that incorporates the two sources of infection. This paper provides an analytical and numerical study of the model that validates trivial insights related to disease spread in a responsive society. As a case study, we applied the model to the COVID-19 spread data from a university environment, namely, the Institut Teknologi Bandung, Indonesia, during its early reopening stage, with a constant number of students. The results show a significant fit between the rendered model and the recorded cases of infections. The extrapolated trajectories indicate the resurgence of cases as students' interaction distance approaches its natural level. The assessment of several strategies is undertaken in this study in order to assist with the school reopening process.

Knowledge, attitude, and practice of Bangladeshi urban slum dwellers towards COVID-19 transmission-prevention: A cross-sectional study

The first COVID-19 case in Bangladesh was detected on March 8, 2020. Since then, efforts are being made across the country to raise awareness among the population for preventing the spread of this virus. We aimed to examine the urban slum dwellers' knowledge, attitude, and practice (KAP) towards COVID-19 transmission-prevention. A phone-based cross-sectional survey was conducted in five slums of Dhaka City. Total 476 adult slum dwellers were interviewed between October 31 to December 1, 2020 using a pre-tested questionnaire. During an interview, information was collected on participants' demographic characteristics and KAP items towards COVID-19. We used quartiles for categorization of knowledge and practice score where the first quartile represents poor, the second and third quartiles represent average while the fourth quartile represents good. Attitude score was standardized using z-score and identified as positive and negative attitude. Multiple linear regression models were used separately to identify the socioeconomic predictors of the KAP scores. The results showed that 25% of the respondents had good knowledge and 25% had poor knowledge, 48% had a positive attitude and 52% had a negative attitude, and 21% maintained good practice and 33% maintained poor practice towards COVID-19 transmission-prevention. About 75% respondents relied on television for COVID-19 related information. Regression results showed that knowledge and attitude scores were significantly higher if respondents had primary or secondary and above level of education compared to the uneducated group. Female respondents maintained significantly good practice compared to their male counterparts (β = 6.841; p<0.01). This study has found that one third of the studied slum dwellers maintained poor practice and one fourth had poor knowledge towards COVID-19 transmission-prevention. As KAP domains are significantly correlated, efforts are needed to raise awareness of COVID-19 particularly targeting individuals with average and lower knowledge to improve attitude and practice for the prevention of COVID-19.

Do disease prevalence and severity drive COVID-19 vaccine demand?

Large scale vaccination of population is widely accepted to be the key to recovery from the devastating economic and public health impacts of the COVID-19 pandemic. However, low uptake of vaccine has challenged vaccination efforts in many parts of the world. The paper explores the determinants of demand for COVID-19 vaccination - specifically, the prevalence dependence hypothesis - that identifies infection prevalence and mortality as the key drivers of individual preventive behavior against infectious diseases. Using daily disease tracking and vaccination data from 47 European countries the paper finds strong evidence that COVID-19 infection rate and mortality rate drive future vaccination uptake. Specifically, results from fixed effects models suggest that while lagged infection prevalence induce vaccination uptake by 0.18 to 0.24 percent, while the effect of lagged mortality is significantly larger, ranging between 1.10 to 1.53 percent. The results highlight the critical role of behavioral response to epidemiological outcomes and are of critical significance for COVID-19 mitigation policies, especially as they relate to achieving vaccine-induced herd immunity and economic reopening.

Sociodemographic factors and self-restraint from social behaviors during the COVID-19 pandemic in Japan: A cross-sectional study

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Japan’s state of emergency is just a request for self-restraint unlike lockdown.

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Survey of sociodemographic factors that affect self-restraint from social behaviors.

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Unnecessary social behaviors and daily necessities shopping had different results.

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Individual interventions may enhance the effects of the request for self-restraint.

The control of human flow has led to better control of COVID-19 infections. Japan’s state of emergency, unlike other countries, is not legally binding but is rather a request for individual self-restraint; thus, factors must be identified that do not respond to self-restraint, and countermeasures considered for those factors to enhance its efficacy. We examined the relationship between sociodemographic factors and self-restraint toward social behaviors during a pandemic in Japan. This cross-sectional study used data for February 18–19, 2021, obtained from an internet survey; 19,560 participants aged 20–65 were included in the analysis. We identified five relevant behaviors: (1) taking a day trip; (2) eating out with five people or more; (3) gathering with friends and colleagues; (4) shopping for other than daily necessities; (5) shopping for daily necessities. Multilevel logistic regression analyses were used to examine the relationship between sociodemographic factors and self-restraint for each of the behaviors. Results showed that for behaviors other than shopping for daily necessities, women, those aged 60–65, married people, highly educated people, high-income earners, desk workers and those who mainly work with interpersonal communication, and those with underlying disease reported more self-restraint. Older people had less self-restraint than younger people toward shopping for daily necessities; an underlying disease had no effect on the identified behavior. Specialized interventions for these groups that include recommendations for greater self-restraint may improve the efficacy of the implementing measures that request self-restraint.

Modelling: Understanding pandemics and how to control them

New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics.

Effects of trust, risk perception, and health behavior on COVID-19 disease burden: Evidence from a multi-state US survey

Early public health strategies to prevent the spread of COVID-19 in the United States relied on non-pharmaceutical interventions (NPIs) as vaccines and therapeutic treatments were not yet available. Implementation of NPIs, primarily social distancing and mask wearing, varied widely between communities within the US due to variable government mandates, as well as differences in attitudes and opinions. To understand the interplay of trust, risk perception, behavioral intention, and disease burden, we developed a survey instrument to study attitudes concerning COVID-19 and pandemic behavioral change in three states: Idaho, Texas, and Vermont. We designed our survey (n = 1034) to detect whether these relationships were significantly different in rural populations. The best fitting structural equation models show that trust indirectly affects protective pandemic behaviors via health and economic risk perception. We explore two different variations of this social cognitive model: the first assumes behavioral intention affects future disease burden while the second assumes that observed disease burden affects behavioral intention. In our models we include several exogenous variables to control for demographic and geographic effects. Notably, political ideology is the only exogenous variable which significantly affects all aspects of the social cognitive model (trust, risk perception, and behavioral intention). While there is a direct negative effect associated with rurality on disease burden, likely due to the protective effect of low population density in the early pandemic waves, we found a marginally significant, positive, indirect effect of rurality on disease burden via decreased trust (p = 0.095). This trust deficit creates additional vulnerabilities to COVID-19 in rural communities which also have reduced healthcare capacity. Increasing trust by methods such as in-group messaging could potentially remove some of the disparities inferred by our models and increase NPI effectiveness.

Exposure to the COVID-19 pandemic environment and generosity

We report data from an online experiment which allows us to study how generosity changed over a 6-day period during the initial explosive growth of the COVID-19 pandemic in Andalusia, Spain, while the country was under a strict lockdown. Participants (n = 969) could donate a fraction of a €100 prize to an unknown charity. Our data are particularly rich in the age distribution and we complement them with daily public information about COVID-19-related deaths, infections and hospital admissions. We find correlational evidence that donations decreased in the period under study, particularly among older individuals. Our analysis of the mechanisms behind the detected decrease in generosity suggests that expectations about others' behaviour, perceived mortality risk and (alarming) information play a key-but independent-role for behavioural adaptation. These results indicate that social behaviour is quickly adjusted in response to the pandemic environment, possibly reflecting some form of selective prosociality.

Clusters of COVID-19 protective and risky behaviors and their associations with pandemic, socio-demographic, and mental health factors in the United States

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Protective and risky behaviors for COVID-19 cluster in a U.S.-based sample.

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Behavior classes had differing patterns of socio-demographics and pandemic exposure.

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Posttraumatic stress and anxiety were elevated among protective and risky classes.

Individual behaviors are critical for preventing the spread of coronavirus disease 2019 (COVID-19) infection. Given that both protective and risky behaviors influence risk of infection, it is critical that we understand how such behaviors cluster together and in whom. Using a data-driven approach, we identified clusters of COVID-19-related protective and risky behaviors and examined associations with socio-demographic, pandemic, and mental health factors. Data came from a cross-sectional online U.S. nationwide study of 832 adults with high levels of pre-pandemic trauma. Latent class analysis was performed with ten protective (e.g., washing hands, wearing masks) and eight risky (e.g., attending indoor restaurants, taking a flight) behaviors for COVID-19. Then, we examined distributions of socio-demographic and pandemic factors across behavior classes using ANOVA or Chi-square tests, and associations between mental health factors (depressive, anxiety, posttraumatic stress symptoms) and behavior classes using multinomial logistic regression. We identified four classes, including three classes with relatively low risky but high (28.8%), moderate (33.5%) and minimal (25.5%) protective behaviors and one high risky behaviors class with associated moderate protective behaviors (12.1%). Age, sexual orientation, political preference, and most pandemic factors differed significantly across behavior classes. Anxiety and posttraumatic stress symptoms, but not depression, were higher in the High Risk, but also Highly and Moderately Protective classes, relative to Minimally Protective. Prevention and intervention efforts should examine constellations of protective and risky behaviors to comprehensively understand risk, and consider current anxiety and posttraumatic stress symptoms as potential risk indicators.

Knowledge Management-Based Nursing Care Educational Training: A Key Strategy to Improve Healthcare Associated Infection Prevention Behavior

Introduction

Knowledge management-based nursing care has a positive effect in preventing healthcare associated infections (HAIs). Therefore, nursing professionals can utilize key strategies of knowledge management to support clinical decision making, reorganize nursing actions, and maximize patient outcomes.

Objectives

The aim of this study was to determine the effect of knowledge management-based nursing care educational training on HAI prevention behavior at the High Care Unit (HCU) of Saiful Anwar Hospital Malang.

Methods

A quasiexperimental design with a pretest, educational training intervention, and posttest were conducted on 15 nurses in the HCU of Saiful Anwar Hospital Malang, which lasted for 16 days. Furthermore, observation of nursing care documentation, nurses’ handwashing compliance, and presence of infection-causing bacteria in the HCU staff and environment (hands rub handle, medical record, and patient's bed) was carried out pre (day 1–7) and post training (day 10–16). Subsequently, educational training related to knowledge management-based nursing care was conducted for 2 days (day 8–9) by the Doktor Mengabdi Team of Universitas Brawijaya.

Results

The knowledge level and completeness of the nursing care documentation in the HCU room significantly increased after the training (p < .05). Also, compliance to the six steps five moments of nurses’ handwashing increased after the training (p > .05). Infection-causing bacteria were found in the HCU environment and staff before and after the training involving Pseudomonas stutzeri, Sphingomonas paucimobilis, Enterobacter cloacae, Staphylococcus aureus, Acinetobacter baumannii, Pasteurella pneumotropica, and Acinetobacter lwoffii. Therefore, increased knowledge of HCU nurses and complete documentation (r = .890; p = .054), increased knowledge of HCU nurses and handwashing compliance (r = .770; p = .086), and handwashing compliance and bacterial presence (r = .816; p = .084) all had a positive correlation.

Conclusion

Knowledge management-based nursing care educational training increased infection prevention behavior in the HCU of Saiful Anwar Hospital Malang.

Human Mobility to Parks Under the COVID-19 Pandemic and Wildfire Seasons in the Western and Central United States

In 2020, people's health suffered a great crisis under the dual effects of the COVID-19 pandemic and the extensive, severe wildfires in the western and central United States. Parks, including city, national, and cultural parks, offer a unique opportunity for people to maintain their recreation behaviors following the social distancing protocols during the pandemic. However, massive forest wildfires in western and central US, producing harmful toxic gases and smoke, pose significant threats to human health and affect their recreation behaviors and mobility to parks. In this study, we employed the geographically and temporally weighted regression (GTWR) Models to investigate how COVID-19 and wildfires jointly shaped human mobility to parks, regarding the number of visits per capita, dwell time, and travel distance to parks, during June - September 2020. We detected strong correlations between visitations and COVID-19 incidence in southern Montana, western Wyoming, Colorado, and Utah before August. However, the pattern was weakened over time, indicating the decreasing trend of the degree of concern regarding the pandemic. Moreover, more park visits and lower dwell time were found in parks further away from wildfires and less air pollution in Washington, Oregon, California, Colorado, and New Mexico, during the wildfire season, suggesting the potential avoidance of wildfires when visiting parks. This study provides important insights on people's responses in recreation and social behaviors when facing multiple severe crises that impact their health and wellbeing, which could support the preparation and mitigation of the health impacts from future pandemics and natural hazards.

Cross-Cultural Validation of a New Version in Spanish of Four Items of the Preventive COVID-19 Infection Behaviors Scale (PCIBS) in Twelve Latin American Countries

The invariance of the Preventive COVID-19 Infection Behaviors Scale (PCIBS) was evaluated in 12 Latin American countries (Argentina, Bolivia, Chile, Colombia, Cuba, Ecuador, El Salvador, Guatemala, Mexico, Paraguay, Peru, and Uruguay). A total of 5183 people from the aforementioned countries participated, selected using the snowball sampling method. Measurement invariance was assessed by multigroup confirmatory factor analysis (MG-CFA) and Multi-Group Factor Analysis Alignment (CFA-MIAL). In addition, item characteristics were assessed based on Item Response Theory. The results indicate that the original five-item version of the PCIBS is not adequate; whereas a four-item version of the PCIBS (PCIBS-4) showed a good fit in all countries. Thus, using the MG-CFA method, the PCIBS-4 achieved metric invariance, while the CFA-MIAL method indicated that the PCIBS-4 shows metric and scalar invariance. Likewise, the four items present increasing difficulties and high values in the discrimination parameters. The comparison of means of the PCIBS-4 reported irrelevant differences between countries; however, Mexico and Peru presented the highest frequency of preventive behaviors related to COVID-19. It is concluded that the PCIBS-4 is a unidimensional self-report measure which is reliable and invariant across the twelve participating Latin American countries. It is expected that the findings will be of interest to social and health scientists, as well as those professionals directly involved in public health decision making.

Nudge strategies for behavior-based prevention and control of neglected tropical diseases: A scoping review and ethical assessment

Background

Nudging, a strategy that uses subtle stimuli to direct people’s behavior, has recently been included as an effective and low-cost behavior change strategy in low- and middle- income countries (LMIC), targeting behavior-based prevention and control of neglected tropical diseases (NTDs). The present scoping review aims to provide a timely overview of how nudge interventions have been applied within this field. In addition, the review proposes a framework for the ethical consideration of nudges for NTD prevention and control, or more broadly global health promotion.

Methods

A comprehensive search was performed in several databases: MEDLINE, PsycINFO, and Embase (Ovid), Web of Science Core Collection, CINAHL, ERIC and Econ.Lit (EBSCO), as well as registered trials and reviews in CENTRAL and PROSPERO to identify ongoing or unpublished studies. Additionally, studies were included through a handpicked search on websites of governmental nudge units and global health or development organizations.

Results

This scoping review identified 33 relevant studies, with only two studies targeting NTDs in particular, resulting in a total of 67 nudge strategies. Most nudges targeted handwashing behavior and were focused on general health practices rather than targeting a specific disease. The most common nudge strategies were those targeting decision assistance, such as facilitating commitment and reminder actions. The majority of nudges were of moderate to high ethical standards, with the highest standards being those that had the most immediate and significant health benefits, and those implemented by agents in a trust relationship with the target audience.

Conclusion

Three key recommendations should inform research investigating nudge strategies in global health promotion in general. Firstly, future efforts should investigate the different opportunities that nudges present for targeting NTDs in particular, rather than relying solely on integrated health promotion approaches. Secondly, to apply robust study designs including rigorous process and impact evaluation which allow for a better understanding of ‘what works’ and ‘how it works’. Finally, to consider the ethical implications of implementing nudge strategies, specifically in LMIC.

Behavior is at the core of neglected tropical disease (NTD) prevention and control, certainly within low-, and middle- income countries (LMIC) where resources are often limited. Therefore, strategies to promote behavior change should be included and investigated in future efforts. Nudging, a low-cost strategy that subtly directs people towards positive behavioral choices, has recently gained attention in global health promotion. Nudge strategies have been applied to a wide range of health-promoting behaviors such as handwashing. To understand which strategies were used, where and how these were applied, and whether these were ethically informed and implemented, we undertook a comprehensive review of the available sources. This resulted in 33 included studies, with a total of 67 nudge strategies for behavior-based prevention and control of NTDs in LMIC. Only two studies targeted NTDs in particular, the other 31 included studies were focused on more general health promoting behaviors, with the majority targeting handwashing with soap. The most common nudge strategies were those targeting decision assistance, such as fostering commitment and reminder actions. In general, the ethical assessment presented favorable results. We identified the need for robust study designs to better understand how nudges can be implemented in the future.

On the effectiveness of communication strategies as non-pharmaceutical interventions to tackle epidemics

When pharmaceutical interventions are unavailable to deal with an epidemic outbreak, adequate management of communication strategies can be key to reduce the contagion risks. On the one hand, accessibility to trustworthy and timely information, whilst on the other, the adoption of preventive behaviors may be both crucial. However, despite the abundance of communication strategies, their effectiveness has been scarcely evaluated or merely circumscribed to the scrutiny of public affairs. To study the influence of communication strategies on the spreading dynamics of an infectious disease, we implemented a susceptible-exposed-infected-removed-dead (SEIRD) epidemiological model, using an agent-based approach. Agents in our systems can obtain information modulating their behavior from two sources: (i) through the local interaction with other neighboring agents and, (ii) from a central entity delivering information with a certain periodicity. In doing so, we highlight how global information delivered from a central entity can reduce the impact of an infectious disease and how informing even a small fraction of the population has a remarkable impact, when compared to not informing the population at all. Moreover, having a scheme of delivering daily messages makes a stark difference on the reduction of cases, compared to the other evaluated strategies, denoting that daily delivery of information produces the largest decrease in the number of cases. Furthermore, when the information spreading relies only on local interactions between agents, and no central entity takes actions along the dynamics, then the epidemic spreading is virtually independent of the initial amount of informed agents. On top of that, we found that local communication plays an important role in an intermediate regime where information coming from a central entity is scarce. As a whole, our results highlight the importance of proper communication strategies, both accurate and daily, to tackle epidemic outbreaks.

The Knowledge, Attitude, and Behavior of Hospitalized Patients' Families in the Effort to Prevent COVID-19

Introduction The coronavirus disease-2019 (COVID-19) pandemic has also hit Indonesia. Until September 2020, cases continued to increase with the highest number in Jakarta. The right behavior needs to be followed to prevent COVID-19; this aspect has a strong relationship with knowledge and attitude. This study aimed to analyze the relationship between the knowledge, attitudes, and behavior of hospitalized patients' families in Fatmawati Hospital, Jakarta, in an effort to prevent COVID-19.

Materials and Methods A cross-sectional study was conducted on 300 respondents using a self-administered questionnaire to assess their knowledge, attitude, and behavior about COVID-19. The relationship between knowledge, attitude, and behavior was analyzed using the chi-square test with p < 0.05.

Results Most of the participants responded to the questionnaire showing a good knowledge, attitude, and behavior related to the efforts to prevent COVID-19. No relationship was present between knowledge, attitude, and behavior in an effort to prevent COVID-19 (p = 0.414 and p = 0.165).

Conclusion The hospitalized patients' families exhibited an adequate level of knowledge, attitude, and preventive behaviors toward COVID-19.

Compassionate goals predict COVID-19 health behaviors during the SARS-CoV-2 pandemic

We predicted that people with compassionate goals to support others and not harm them practiced more COVID-19 health behaviors during the SARS-CoV-2 pandemic to protect both themselves and others from infection. Three studies (N = 1,143 American adults) supported these predictions and ruled out several alternative explanations. Compassionate goals unrelated to the health context predicted COVID-19 health behaviors better than the general motivation to be healthy (Studies 2 and 3). In contrast, general health motivation predicted general health behaviors better than did compassionate goals. Compassionate goals and political ideology each explained unique variance in COVID-19 health behaviors (Studies 1-3). Compassionate goals predict unique variance in COVID-19 health behaviors beyond empathic concern, communal orientation, and relational self-construal (Study 3), supporting the unique contribution of compassionate goals to understanding health behaviors. Our results suggest that ecosystem motivation is an important predictor of health behaviors, particularly in the context of a highly contagious disease.

Triple contagion: a two-fears epidemic model

We present a differential equations model in which contagious disease transmission is affected by contagious fear of the disease and contagious fear of the control, in this case vaccine. The three contagions are coupled. The two fears evolve and interact in ways that shape distancing behaviour, vaccine uptake, and their relaxation. These behavioural dynamics in turn can amplify or suppress disease transmission, which feeds back to affect behaviour. The model reveals several coupled contagion mechanisms for multiple epidemic waves. Methodologically, the paper advances infectious disease modelling by including human behavioural adaptation, drawing on the neuroscience of fear learning, extinction and transmission.

Analytic Thinking and Political Orientation in the Corona Crisis

With much unknown about the new coronavirus, the scientific consensus is that human hosts are crucial to its spread and reproduction-the more people behave like regular socializing beings they are, the more likely it is that the virus will propagate. Hence, many nations worldwide have mandated physical-distancing measures. In the current preregistered research, we focus on examining two factors that may help explain differences in adherence to COVID-19 preventive behaviors and policy support across different countries-political orientation and analytic thinking. We positioned our research within the dual-process framework of human reasoning and investigated the role of cognitive reflection, open-minded thinking, and political ideology in determining COVID-19 responsible behavior (physical distancing and maintaining hygiene) and support for restrictive COVID-19 policies on a sample of 12,490 participants from 17 countries. We have not been able to detect substantial relationships of political orientation with preventive behaviors and policy support, and overall found no reliable evidence of politicization, nor polarization regarding the issue. The results of structural equation modeling showed that the inclination towards COVID-19 preventive measures and their endorsement were defined primarily by the tendency of open-minded thinking. Specifically, open-minded thinking was shown to be a predictor of all three criteria-avoiding physical contact, maintaining physical hygiene, and supporting COVID-19 restrictive mitigation policies. Cognitive reflection was predictive of lesser adherence to stricter hygiene and only very weakly predictive of lesser policy support. Furthermore, there was no evidence of these effects varying across political contexts. The mediation analysis suggested a partial mediation effect of COVID-19 conspiracy beliefs on the relationships of open-mindedness and cognitive reflection with physical distancing (but not adherence to stricter hygiene) and COVID-19 policy support, albeit very small and significant primarily due to sample size. There was also no evidence of these effects varying across political contexts. Finally, we have not been able to find strong evidence of political orientation modifying the relationship between analytical thinking and COVID-19 behaviors and policy support, although we explored the pattern of these effects in the US and Canadian samples for exploratory purposes and comparison with other similar studies.

A review and agenda for integrated disease models including social and behavioural factors

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 Media Use, Self-Efficacy, Perceived Threat, and Preventive Behavior in Times of COVID-19: Results of a Cross-Sectional Study in Pakistan

Although the role of social media in infectious disease outbreaks is receiving increasing attention, little is known about the mechanisms by which social media use affects risk perception and preventive behaviors during such outbreaks. This study aims to determine whether there are any relationships between social media use, preventive behavior, perceived threat of coronavirus, self-efficacy, and socio-demographic characteristics. The data were collected from 310 respondents across Pakistan using an online cross-sectional survey. Reliability analyses were performed for all scales and structural equational modeling was used to identify the relationships between study variables. We found that: (i) social media use predicts self-efficacy (β = 0.25, p < 0.05) and perceived threat of coronavirus (β = 0.54, p < 0.05, R 2 = 0.06), and (ii) preventive behavior is predicted by self-efficacy and perceived threat of coronavirus (R = 0.10, p < 0.05). Therefore, these results indicate the importance of social media's influence on health-related behaviors. These findings are valuable for health administrators, governments, policymakers, and social scientists, specifically for individuals whose situations are similar to those in Pakistan.

Development and validation of a questionnaire to assess preventive practices against COVID-19 pandemic in the general population

Coronavirus Disease 2019 (COVID-19) pandemic has affected millions of people worldwide with far-reaching socio-economic implications in society. The adoption of preventive practices by the public remains the mainstay in reducing the spread of COVID-19 but there is a dearth of validated tools to assess such infection prevention practices related to pandemics. This study was conducted to develop and validate a questionnaire for the assessment of preventive practices against COVID-19 in the general population. It was done following a standardized protocol involving questionnaire development through literature review, focused group discussions, in-depth interviews, expert opinion, and pre-testing. This was followed by the validation of the questionnaire through a cross-sectional survey on 108 individuals from diverse backgrounds in New Delhi, India in July 2020. Exploratory factor analysis was used to evaluate construct validity. Internal consistency was assessed by Cronbach's alpha coefficient. The developed questionnaire for assessing preventive practices consists of two sections: the first section of 18 items to evaluate preventive practices and the second section of 19 items for assessing various reasons for deficiencies in the preventive practices. The first section has good content validity (CVR = 0.81 and S-CVI/Av = 0.97) and internal consistency (Cronbach's alpha coefficient 0.82). Thus, this questionnaire is a valid and reliable tool for the comprehensive assessment of preventive practices and barriers related to the COVID-19 pandemic. It will be useful in assessing the preparedness of the public and will be helpful to policymakers in designing appropriate interventions for protection against COVID-19.

Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study

Background

To combat the global COVID-19 pandemic, contact tracing apps have been discussed as digital health solutions to track infection chains and provide appropriate information. However, observational studies point to low acceptance in most countries, and few studies have yet examined theory-based predictors of app use in the general population to guide health communication efforts.

Objective

This study utilizes established health behavior change and technology acceptance models to predict adoption intentions and frequency of current app use.

Methods

We conducted a cross-sectional online survey between May and July 2020 in a German convenience sample (N=349; mean age 35.62 years; n=226, 65.3% female). To inspect the incremental validity of model constructs as well as additional variables (privacy concerns, personalization), hierarchical regression models were applied, controlling for covariates.

Results

The theory of planned behavior and the unified theory of acceptance and use of technology predicted adoption intentions (R²=56%-63%) and frequency of current app use (R²=33%-37%). A combined model only marginally increased the predictive value by about 5%, but lower privacy concerns and higher threat appraisals (ie, anticipatory anxiety) significantly predicted app use when included as additional variables. Moreover, the impact of perceived usefulness was positive for adoption intentions but negative for frequency of current app use.

Conclusions

This study identified several theory-based predictors of contact tracing app use. However, few constructs, such as social norms, have a consistent positive effect across models and outcomes. Further research is required to replicate these observations, and to examine the interconnectedness of these constructs and their impact throughout the pandemic. Nevertheless, the findings suggest that promulgating affirmative social norms and positive emotional effects of app use, as well as addressing health concerns, might be promising strategies to foster adoption intentions and app use in the general population.

Cognitive Predictors of Precautionary Behavior During the COVID-19 Pandemic

The attempts to mitigate the unprecedented health, economic, and social disruptions caused by the COVID-19 pandemic are largely dependent on establishing compliance to behavioral guidelines and rules that reduce the risk of infection. Here, by conducting an online survey that tested participants' knowledge about the disease and measured demographic, attitudinal, and cognitive variables, we identify predictors of self-reported social distancing and hygiene behavior. To investigate the cognitive processes underlying health-prevention behavior in the pandemic, we co-opted the dual-process model of thinking to measure participants' propensities for automatic and intuitive thinking vs. controlled and reflective thinking. Self-reports of 17 precautionary behaviors, including regular hand washing, social distancing, and wearing a face mask, served as a dependent measure. The results of hierarchical regressions showed that age, risk-taking propensity, and concern about the pandemic predicted adoption of precautionary behavior. Variance in cognitive processes also predicted precautionary behavior: participants with higher scores for controlled thinking (measured with the Cognitive Reflection Test) reported less adherence to specific guidelines, as did respondents with a poor understanding of the infection and transmission mechanism of the COVID-19 virus. The predictive power of this model was comparable to an approach (Theory of Planned Behavior) based on attitudes to health behavior. Given these results, we propose the inclusion of measures of cognitive reflection and mental model variables in predictive models of compliance, and future studies of precautionary behavior to establish how cognitive variables are linked with people's information processing and social norms.

Incorporating Social Determinants of Health into Modelling of COVID-19 and other Infectious Diseases: A Baseline Socio-economic Compartmental Model

The role of socio-economic conditions has been largely implicit in mathematical epidemiological models. However, measures to address the current pandemic, specifically the relevant interventions proposing physical distancing, have highlighted how social determinants affect contagion and mortality dynamics of COVID-19. For the most part, these social determinants are not present in either policy discussions or in epidemiological models. We argue for the importance of incorporating social determinants of health into the modelling dynamics of COVID-19, and show how global variation of these conditions may be integrated into relevant models. In doing so, we also highlight a key political economy aspect of reproduction dynamics in epidemics.

Development of the reproduction number from coronavirus SARS-CoV-2 case data in Germany and implications for political measures

BACKGROUND

SARS-CoV-2 has induced a worldwide pandemic and subsequent non-pharmaceutical interventions (NPIs) to control the spread of the virus. As in many countries, the SARS-CoV-2 pandemic in Germany has led to a consecutive roll-out of different NPIs. As these NPIs have (largely unknown) adverse effects, targeting them precisely and monitoring their effectiveness are essential. We developed a compartmental infection dynamics model with specific features of SARS-CoV-2 that allows daily estimation of a time-varying reproduction number and published this information openly since the beginning of April 2020. Here, we present the transmission dynamics in Germany over time to understand the effect of NPIs and allow adaptive forecasts of the epidemic progression.

METHODS

We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states using our model. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread in different regions of Italy, the model was optimized to fit data from the Robert Koch Institute.

RESULTS

The time-varying reproduction number (Rt) in Germany decreased to <1 in early April 2020, 2-3 weeks after the implementation of NPIs. Partial release of NPIs both nationally and on federal state level correlated with moderate increases in Rt until August 2020. Implications of state-specific Rt on other states and on national level are characterized. Retrospective evaluation of the model shows excellent agreement with the data and usage of inpatient facilities well within the healthcare limit. While short-term predictions may work for a few weeks, long-term projections are complicated by unpredictable structural changes.

CONCLUSIONS

The estimated fraction of immunized population by August 2020 warns of a renewed outbreak upon release of measures. A low detection rate prolongs the delay reaching a low case incidence number upon release, showing the importance of an effective testing-quarantine strategy. We show that real-time monitoring of transmission dynamics is important to evaluate the extent of the outbreak, short-term projections for the burden on the healthcare system, and their response to policy changes.

Trust, risk perception, and COVID-19 infections: Evidence from multilevel analyses of combined original dataset in China

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.

Examining the application of behaviour change theories in the context of infectious disease outbreaks and emergency response: a review of reviews

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.

Clean and Contain: Initial Development of a Measure of Infection Prevention Behaviors During the COVID-19 Pandemic

Background

The Centers for Disease Control and Prevention (CDC) offer behavioral guidance to prevent the spread of infectious diseases like COVID-19. Cleaning (e.g., cleaning surfaces, washing and sanitizing hands) and containing (e.g., covering coughs, keeping distance from others, especially sick people) behaviors are recommended.

Purpose

To develop the Clean and Contain Measure, a brief measure of compliance with CDC recommendations for prevention of infectious disease, and validate the measure in individuals experiencing the COVID-19 pandemic.

Methods

Participants were recruited from Amazon Mechanical Turk and social media.

Results

In Study 1 (N = 97), exploratory factor analysis revealed two scales: (a) five items assessing cleaning behaviors and (b) four items assessing containing behaviors. Simple structure was obtained and alpha coefficients for both scales were >.83. In Studies 2 (N = 204) and 3 (N = 527), confirmatory factor analysis verified the identical factor structure found in Study 1. All loadings were statistically significant at p < .001. Alpha coefficients for both scales were >.84 for Studies 2 and 3.

Conclusions

Our measure is a reliable and valid indicator of compliance with cleaning and containing health behaviors that help to prevent the spread of diseases like COVID-19. Future research should replicate construct validity in more diverse samples and continue to refine items, examine construct validity, including predictive and discriminant validity, and improve the measure for future use. With continued use and refinement, this measure could allow health officials and researchers to accurately assess compliance with important infection prevention behavior guidelines.

The Prediction of Infectious Diseases: A Bibliometric Analysis

OBJECTIVE

The outbreak of infectious diseases has a negative influence on public health and the economy. The prediction of infectious diseases can effectively control large-scale outbreaks and reduce transmission of epidemics in rapid response to serious public health events. Therefore, experts and scholars are increasingly concerned with the prediction of infectious diseases. However, a knowledge mapping analysis of literature regarding the prediction of infectious diseases using rigorous bibliometric tools, which are supposed to offer further knowledge structure and distribution, has been conducted infrequently. Therefore, we implement a bibliometric analysis about the prediction of infectious diseases to objectively analyze the current status and research hotspots, in order to provide a reference for related researchers.

METHODS

We viewed "infectious disease*" and "prediction" or "forecasting" as search theme in the core collection of Web of Science from inception to 1 May 2020. We used two effective bibliometric tools, i.e., CiteSpace (Drexel University, Philadelphia, PA, USA) and VOSviewer (Leiden University, Leiden, The Netherlands) to objectively analyze the data of the prediction of infectious disease domain based on related publications, which can be downloaded from the core collection of Web of Science. Then, the leading publications of the prediction of infectious diseases were identified to detect the historical progress based on collaboration analysis, co-citation analysis, and co-occurrence analysis.

RESULTS

1880 documents that met the inclusion criteria were extracted from Web of Science in this study. The number of documents exhibited a growing trend, which can be expressed an increasing number of experts and scholars paying attention to the field year by year. These publications were published in 427 different journals with 11 different document types, and the most frequently studied types were articles 1618 (83%). In addition, as the most productive country, the United States has provided a lot of scientific research achievements in the field of infectious diseases.

CONCLUSION

Our study provides a systematic and objective view of the field, which can be useful for readers to evaluate the characteristics of publications involving the prediction of infectious diseases and for policymakers to take timely scientific responses.

Social Distancing and Stigma: Association Between Compliance With Behavioral Recommendations, Risk Perception, and Stigmatizing Attitudes During the COVID-19 Outbreak

Introduction: Following behavioral recommendations is key to successful containment of the COVID-19 pandemic. Therefore, it is important to identify causes and patterns of non-compliance in the population to further optimize risk and health communication.

Methods: A total of 157 participants [80% female; mean age = 27.82 years (SD = 11.01)] were surveyed regarding their intention to comply with behavioral recommendations issued by the German government. Latent class analysis examined patterns of compliance, and subsequent multinomial logistic regression models tested sociodemographic (age, gender, country of origin, level of education, region, and number of persons per household) and psychosocial (knowledge about preventive behaviors, risk perception, stigmatizing attitudes) predictors.

Results: Three latent classes were identified: high compliance (25%) with all recommendations; public compliance (51%), with high compliance regarding public but not personal behaviors; and low compliance (24%) with most recommendations. Compared to high compliance, low compliance was associated with male gender [relative risk ratio (RRR) = 0.08 (0.01; 0.85)], younger age [RRR = 0.72 (0.57; 0.93)], and lower public stigma [RRR = 0.21 (0.05; 0.88)]. Low compliers were also younger than public compliers [RRR = 0.76 (0.59; 0.98)].

Discussion: With 25% of the sample reporting full compliance, and 51% differing in terms of public and personal compliance, these findings challenge the sustainability of strict regulatory measures. Moreover, young males were most likely to express low compliance, stressing the need for selective health promotion efforts. Finally, the positive association between public stigma and compliance points to potential othering effects of stigma during a pandemic, but further longitudinal research is required to examine its impact on health and social processes throughout the pandemic.

Decisions and disease: a mechanism for the evolution of cooperation

In numerous contexts, individuals may decide whether they take actions to mitigate the spread of disease, or not. Mitigating the spread of disease requires an individual to change their routine behaviours to benefit others, resulting in a 'disease dilemma' similar to the seminal prisoner's dilemma. In the classical prisoner's dilemma, evolutionary game dynamics predict that all individuals evolve to 'defect.' We have discovered that when the rate of cooperation within a population is directly linked to the rate of spread of the disease, cooperation evolves under certain conditions. For diseases which do not confer immunity to recovered individuals, if the time scale at which individuals receive accurate information regarding the disease is sufficiently rapid compared to the time scale at which the disease spreads, then cooperation emerges. Moreover, in the limit as mitigation measures become increasingly effective, the disease can be controlled; the number of infections tends to zero. It has been suggested that disease spreading models may also describe social and group dynamics, indicating that this mechanism for the evolution of cooperation may also apply in those contexts.

A social identity perspective on COVID-19: Health risk is affected by shared group membership

In the face of a novel infectious disease, changing our collective behaviour is critical to saving lives. One determinant of risk perception and risk behaviour that is often overlooked is the degree to which we share psychological group membership with others. We outline, and summarize supporting evidence for, a theoretical model that articulates the role of shared group membership in attenuating health risk perception and increasing health risk behaviour. We emphasize the importance of attending to these processes in the context of the ongoing response to COVID‐19 and conclude with three recommendations for how group processes can be harnessed to improve this response.

Towards a data-driven characterization of behavioral changes induced by the seasonal flu

In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 - 18 and 2018 - 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals' characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious diseases.