Behavioural change models for infectious disease transmission: a systematic review (2010-2015)

Behavior-driven forecasts of neighborhood-level COVID-19 spread in New York City

The COVID-19 pandemic in New York City (NYC) was characterized by marked disparities in disease burdens across neighborhoods. Accurate neighborhood-level forecasts are critical for planning more equitable resource allocation to reduce health inequalities; however, such spatially high-resolution forecasts remain scarce in operational use. In this study, we analyze aggregated foot traffic data derived from mobile devices to measure the connectivity among 42 NYC neighborhoods driven by various human activities such as dining, shopping, and entertainment. Using real-world time-varying contact patterns in different place categories, we develop a parsimonious behavior-driven epidemic model that incorporates population mixing, indoor crowdedness, dwell time, and seasonality of virus transmissibility. We fit this model to neighborhood-level COVID-19 case data in NYC and further couple this model with a data assimilation algorithm to generate short-term forecasts of neighborhood-level COVID-19 cases in 2020. We find differential contact patterns and connectivity between neighborhoods driven by different human activities. The behavior-driven model supports accurate modeling of neighborhood-level SARS-CoV-2 transmission throughout 2020. In the best-fitting model, we estimate that the force of infection (FOI) in indoor settings increases sublinearly with crowdedness and dwell time. Retrospective forecasting demonstrates that this behavior-driven model generates improved short-term forecasts in NYC neighborhoods compared to several baseline models. Our findings indicate that aggregated foot-traffic data for routine human activities can support neighborhood-level COVID-19 forecasts in NYC. This behavior-driven model may be adapted for use with other respiratory pathogens sharing similar transmission routes.

Subject Modeling-Based Analysis of the Evolution and Intervention Strategies of Major Emerging Infectious Disease Events

Objective

Due to the popularity of the Internet and the extensive use of new media, after the occurrence of infectious diseases, the spread of social media information greatly affects the group's opinion and cognition and even the health behaviors they take, thus affecting the spread of infectious diseases. Therefore, this paper studies the event evolution from multiple dimensions.

Methods

To address this gap, we developed a three-layer model framework of major infectious disease event evolution based on subject modeling. This framework integrates three key factors-health transmission, perspective interaction, and risk perception-to analyze group perspective evolution, behavioral change, and virus transmission processes. The model's effectiveness was evaluated through simulation and sensitivity analysis. In addition, we conducted an empirical analysis by constructing a social media health transmission effect index system to identify the critical factors affecting health transmission.

Results

Simulation results reveal that among the three factors, health transmission has the most significant impact on the evolution of group perspectives during infectious disease events. Moreover, the dynamics of public viewpoint evolution influence individual decisions regarding the adoption of non-pharmacological interventions, which are shown to effectively reduce both the transmission rate of the virus and the peak number of infections.

Conclusion

The findings of this study enhance our understanding of the complex mechanisms and evolutionary pathways in infectious disease events. By integrating multiple dimensions of event evolution, the proposed model offers valuable insights for the design of effective countermeasures and strategies in emergency management and response to infectious disease outbreaks.

The effect of heterogeneity of relative vaccine costs on the mean population vaccination rate with mpox as an example

Mpox (formerly known as monkeypox) is a neglected tropical disease that became notorious during its 2022-2023 worldwide outbreak. The vaccination was available, but there were inequities in vaccine access. In this paper, we extend existing game-theoretic models to study a population that is heterogeneous in the relative vaccination costs. We consider a population with two groups. We determine the Nash equilibria (NE), i.e., optimal vaccination rates, for each of the groups. We show that the NE always exists and that, for a narrow range of parameter values, there can be multiple NEs. We focus on comparing the mean optimal vaccination rate in the heterogeneous population with the optimal vaccination rate in the corresponding homogeneous population. We show that there is a critical size for the group with lower relative costs and the mean optimal vaccination in the heterogeneous population is more than in the homogeneous population if and only if the group is larger than the critical size.

Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media

Understanding how misinformation affects the spread of disease is crucial for public health, especially given recent research indicating that misinformation can increase vaccine hesitancy and discourage vaccine uptake. However, it is difficult to investigate the interaction between misinformation and epidemic outcomes due to the dearth of data-informed holistic epidemic models. Here, we employ an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties derived from social media data. The model allows us to simulate various scenarios to understand how epidemic spreading can be affected by misinformation spreading through one particular social media platform. Using this model, we compare a worst-case scenario, in which individuals become misinformed after a single exposure to low-credibility content, to a best-case scenario where the population is highly resilient to misinformation. We estimate the additional portion of the U.S. population that would become infected over the course of the COVID-19 epidemic in the worst-case scenario. This work can provide policymakers with insights about the potential harms of exposure to online vaccine misinformation.

Behaviour quantification of public health policy adoption - the case of non-pharmaceutical measures during COVID-19

In this work, we provide estimates of non-pharmaceutical interventions (NPIs) adoption and its effects on the COVID-19 disease transmission across the province of Ontario, Canada, in 2020. Using freely available data, we estimate perceived risks of infection and a personal discomfort with complying with NPIs for Ontarians across 34 public health units. With the use of game theory, we model a time series of decision making processes in each public health region to extract an estimate of the adoption level of NPIs from March to December 2020. In conjunction with a susceptible-exposed-recovered-isolated compartmental model for Ontario, we are able to estimate a province-wide effectiveness level of NPIs. Last but not least, we show the model's versatility by applying it to Pennsylvania and Georgia in the United States.

Understanding Nash epidemics

Faced with a dangerous epidemic humans will spontaneously social distance to reduce their risk of infection at a socioeconomic cost. Compartmentalized epidemic models have been extended to include this endogenous decision making: Individuals choose their behavior to optimize a utility function, self-consistently giving rise to population behavior. Here, we study the properties of the resulting Nash equilibria, in which no member of the population can gain an advantage by unilaterally adopting different behavior. We leverage an analytic solution that yields fully time-dependent rational population behavior to obtain, 1) a simple relationship between rational social distancing behavior and the current number of infections; 2) scaling results for how the infection peak and number of total cases depend on the cost of contracting the disease; 3) characteristic infection costs that divide regimes of strong and weak behavioral response; 4) a closed form expression for the value of the utility. We discuss how these analytic results provide a deep and intuitive understanding of the disease dynamics, useful for both individuals and policymakers. In particular, the relationship between social distancing and infections represents a heuristic that could be communicated to the population to encourage, or "bootstrap," rational behavior.

Behavioural Change Piecewise Constant Spatial Epidemic Models

Human behaviour significantly affects the dynamics of infectious disease transmission as people adjust their behavior in response to outbreak intensity, thereby impacting disease spread and control efforts. In recent years, there have been efforts to incorporate behavioural change into spatio-temporal individual-level models within a Bayesian MCMC framework. In this past work, parametric spatial risk functions were employed, depending on strong underlying assumptions regarding disease transmission mechanisms within the population. However, selecting appropriate parametric functions can be challenging in real-world scenarios, and incorrect assumptions may lead to erroneous conclusions. As an alternative, non-parametric approaches offer greater flexibility. The goal of this study is to investigate the utilization of semi-parametric spatial models for infectious disease transmission, integrating an "alarm function" to account for behavioural change based on infection prevalence over time within a Bayesian MCMC framework. In this paper, we discuss findings from both simulated and real-life epidemics, focusing on constant piecewise distance functions with fixed change points. We also demonstrate the selection of the change points using the Deviance Information Criteria (DIC).

Analyzing disease control through testing game approach embedded with treatment and vaccination strategies

This research introduces an expanded SEIR (Susceptible-Exposed-Infected-Recovered) model that incorporates the components of testing, treatment, and vaccination. The study utilizes an evolutionary game theory (EGT) framework to investigate the impact of human behavior on the acceptance and implementation of these interventions. The choice to undergo testing and vaccination is considered a strategic decision influenced by perceived risks and benefits. Regarding disease dynamics, adherence to vaccination and testing protocols is seen as a behavioral factor. The present study employs a finite difference method to numerically examine the impact of proactive vaccination and retroactive treatment policies on human behavior. The investigation focuses on these policies' individual and combined effects, considering various factors, including vaccination and testing costs, vaccine efficacy, awareness level, and infection rates. The findings indicate that the integration of heightened awareness and enhanced vaccination efficacy can successfully alleviate the transmission of diseases, even in situations where the expenses associated with testing and vaccination are substantial. Reducing infections in situations characterized by low or moderate awareness or vaccination effectiveness is contingent upon low testing costs. The final epidemic size (FES) negatively correlates with testing and vaccine costs, indicating that lower costs are linked to a lower FES. Optimal vaccine coverage (VC) occurs when vaccine costs are minimal and vaccine efficiency is efficient, whereas treatment coverage (TC) reaches its peak when testing costs are minimal. This research underscores the significance of considering human behavior and the intricate relationship between vaccination, testing, and treatment approaches in managing the transmission of contagious illnesses. It offers valuable perspectives for policymakers to mitigate the consequences of epidemics.

A simultaneous simulation of human behavior dynamics and epidemic spread: A multi-country study amidst the COVID-19 pandemic

The transmission dynamics of infectious diseases and human responses are intertwined, forming complex feedback loops. However, many epidemic models fail to endogenously represent human behavior change. In this study, we introduce a novel behavioral epidemic model that incorporates various behavioral phenomena into SEIR models, including risk-response dynamics, shifts in containment policies, adherence fatigue, and societal learning, alongside disease transmission dynamics. By testing our model against data from 8 countries, where extensive behavioral data were available, we simultaneously replicate death rates, mobility trends, fatigue levels, and policy changes, both in-sample and out-of-sample. Our model offers a comprehensive depiction of changes in multiple behavioral measures along with the spread of the disease. We assess the explanatory power of each model mechanism in capturing data variability. Our findings demonstrate that the comprehensive model that includes all mechanisms provides the most insightful perspective for understanding the influence of human behavior during pandemics.

Adaptive behaviour during epidemics: a social risk appraisal approach to modelling dynamics

The interaction of infectious diseases and behavioural responses to them has been the subject of widespread study. However, limited attention has been given to how broader social context shapes behavioural response. In this work, we propose a novel framework which combines two well-studied dynamic processes into a 'social risk appraisal' mechanism. Our proposed framework has both theoretical and empirical support, occupying an important middle ground in the interacting contagions literature. Results indicate that a risk appraisal framework can express a wide range of epidemic outcomes, driven by simple interaction rules. This framework has implications for designing containment strategies in disease outbreaks, as well as equity considerations. Finally, the risk appraisal approach is well-posed to engage with a broad set of literature in epidemic management, decision-making and the adoption of social behaviours.

Coupled infectious disease and behavior dynamics. A review of model assumptions

To comprehend the dynamics of infectious disease transmission, it is imperative to incorporate human protective behavior into models of disease spreading. While models exist for both infectious disease and behavior dynamics independently, the integration of these aspects has yet to yield a cohesive body of literature. Such an integration is crucial for gaining insights into phenomena like the rise of infodemics, the polarization of opinions regarding vaccines, and the dissemination of conspiracy theories during a pandemic. We make a threefold contribution. First, we introduce a framework todescribemodels coupling infectious disease and behavior dynamics, delineating four distinct update functions. Reviewing existing literature, we highlight a substantial diversity in the implementation of each update function. This variation, coupled with a dearth of model comparisons, renders the literature hardly informative for researchers seeking to develop models tailored to specific populations, infectious diseases, and forms of protection. Second, we advocate an approach tocomparingmodels' assumptions about human behavior, the model aspect characterized by the strongest disagreement. Rather than representing the psychological complexity of decision-making, we show that 'influence-response functions' allow one to identify which model differences generate different disease dynamics and which do not, guiding both model development and empirical research testing model assumptions. Third, we propose recommendations for future modeling endeavors and empirical research aimed atselectingmodels of coupled infectious disease and behavior dynamics. We underscore the importance of incorporating empirical approaches from the social sciences to propel the literature forward.

Social dilemma of nonpharmaceutical interventions: Determinants of dynamic compliance and behavioral shifts

In fighting infectious diseases posing a global health threat, ranging from influenza to Zika, nonpharmaceutical interventions (NPI), such as social distancing and face covering, remain mitigation measures public health can resort to. However, the success of NPI lies in sufficiently high levels of collective compliance, otherwise giving rise to recurrent infections that are not only driven by pathogen evolution but also changing vigilance in the population. Here, we show that compliance with each NPI measure can be highly dynamic and context-dependent during an ongoing epidemic, where individuals may prefer one to another or even do nothing, leading to intricate temporal switching behavior of NPI adoptions. By characterizing dynamic regimes through the perceived costs of NPI measures and their effectiveness in particular regarding face covering and social distancing, our work offers insights into overcoming barriers in NPI adoptions.

How mathematical modelling can inform outbreak response vaccination

Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns in disease spread, simulate control options to assist public health authorities in decision-making, and longer-term operational and financial planning. In the context of vaccine-preventable diseases (VPDs), vaccines are one of the most-cost effective outbreak response interventions, with the potential to avert significant morbidity and mortality through timely delivery. Models can contribute to the design of vaccine response by investigating the importance of timeliness, identifying high-risk areas, prioritising the use of limited vaccine supply, highlighting surveillance gaps and reporting, and determining the short- and long-term benefits. In this review, we examine how models have been used to inform vaccine response for 10 VPDs, and provide additional insights into the challenges of outbreak response modelling, such as data gaps, key vaccine-specific considerations, and communication between modellers and stakeholders. We illustrate that while models are key to policy-oriented outbreak vaccine response, they can only be as good as the surveillance data that inform them.

Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach

Individuals' perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. In this study, we propose an approach to integrate survey data on contact patterns and disease perception into a data-driven compartmental model, by hypothesizing that perceived severity is a determinant of behavioral change. We explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals' behaviors vary based on their perceived severity of the disease. Results indicate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, in a way depending on the interplay between two contrasting effects. On the one hand, longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Differences in behavior across different population groups may impact strongly the epidemiological curves, with a transition from a scenario with two successive epidemic peaks to one with only one (higher) peak and overall more numerous severe outcomes and deaths. The specific modeling choices for how perceived severity modulates behavior parameters do not strongly impact the model's outcomes. Moreover, the study of several simplified models indicate that the observed phenomenology depends on the combination of data describing age-stratified contact patterns and of the feedback loop between disease perception and behavior, while it is robust with respect to the lack of precise information on the distribution of perceived severity in the population. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and gives hints on the type of data that further extensive data collection should target to enhance model accuracy and relevance.

Effect of homophily on coupled behavior-disease dynamics near a tipping point

Understanding the interplay between social activities and disease dynamics is crucial for effective public health interventions. Recent studies using coupled behavior-disease models assumed homogeneous populations. However, heterogeneity in population, such as different social groups, cannot be ignored. In this study, we divided the population into social media users and non-users, and investigated the impact of homophily (the tendency for individuals to associate with others similar to themselves) and online events on disease dynamics. Our results reveal that homophily hinders the adoption of vaccinating strategies, hastening the approach to a tipping point after which the population converges to an endemic equilibrium with no vaccine uptake. Furthermore, we find that online events can significantly influence disease dynamics, with early discussions on social media platforms serving as an early warning signal of potential disease outbreaks. Our model provides insights into the mechanisms underlying these phenomena and underscores the importance of considering homophily in disease modeling and public health strategies.

A retrospective assessment of forecasting the peak of the SARS-CoV-2 Omicron BA.1 wave in England

We discuss the invasion of the Omicron BA.1 variant into England as a paradigm for real-time model fitting and projection. Here we use a mixture of simple SIR-type models, analysis of the early data and a more complex age-structure model fit to the outbreak to understand the dynamics. In particular, we highlight that early data shows that the invading Omicron variant had a substantial growth advantage over the resident Delta variant. However, early data does not allow us to reliably infer other key epidemiological parameters-such as generation time and severity-which influence the expected peak hospital numbers. With more complete epidemic data from January 2022 are we able to capture the true scale of the epidemic in terms of both infections and hospital admissions, driven by different infection characteristics of Omicron compared to Delta and a substantial shift in estimated precautionary behaviour during December. This work highlights the challenges of real time forecasting, in a rapidly changing environment with limited information on the variant's epidemiological characteristics.

Mathematical analysis of simple behavioral epidemic models

COVID-19 highlighted the importance of considering human behavior change when modeling disease dynamics. This led to developing various models that incorporate human behavior. Our objective is to contribute to an in-depth, mathematical examination of such models. Here, we consider a simple deterministic compartmental model with endogenous incorporation of human behavior (i.e., behavioral feedback) through transmission in a classic Susceptible-Exposed-Infectious-Recovered (SEIR) structure. Despite its simplicity, the SEIR structure with behavior (SEIRb) was shown to perform well in forecasting, especially compared to more complicated models. We contrast this model with an SEIR model that excludes endogenous incorporation of behavior. Both models assume permanent immunity to COVID-19, so we also consider a modification of the models which include waning immunity (SEIRS and SEIRSb). We perform equilibria, sensitivity, and identifiability analyses on all models and examine the fidelity of the models to replicate COVID-19 data across the United States. Endogenous incorporation of behavior significantly improves a model's ability to produce realistic outbreaks. While the two endogenous models are similar with respect to identifiability and sensitivity, the SEIRSb model, with the more accurate assumption of the waning immunity, strengthens the initial SEIRb model by allowing for the existence of an endemic equilibrium, a realistic feature of COVID-19 dynamics. When fitting the model to data, we further consider the addition of simple seasonality affecting disease transmission to highlight the explanatory power of the models.

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.

Changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS): a two-year prospective cohort study in Israel

Background

Limited knowledge exists regarding behavioral and biomarker shifts during the period from respiratory infection exposure to testing decisions (the diagnostic decision period), a key phase affecting transmission dynamics and public health strategy development. This study aims to examine the changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS).

Methods

We analyzed data from a two-year prospective cohort study involving 4795 participants in Israel, incorporating smartwatch data, self-reported symptoms, and medical records. Our analysis focused on three critical phases: the digital incubation period (from exposure to physiological anomalies detected by smartwatches), the symptomatic incubation period (from exposure to onset of symptoms), and the diagnostic decision period for influenza, COVID-19, and GAS.

Findings

The delay between initial symptom reporting and testing was 39 [95% confidence interval (CI): 34-45] hours for influenza, 53 [95% CI: 49-58] hours for COVID-19, and 38 [95% CI: 32-46] hours for GAS, with 73 [95% CI: 67-78] hours from anomalies in heart measures to symptom onset for influenza, 23 [95% CI: 18-27] hours for COVID-19, and 62 [95% CI: 54-68] hours for GAS. Analyzing the entire course of infection of each individual, the greatest changes in heart rates were detected 67.6 [95% CI: 62.8-72.5] hours prior to testing for influenza, 64.1 [95% CI: 61.4-66.7] hours prior for COVID-19, and 58.2 [95% CI: 52.1-64.2] hours prior for GAS. In contrast, the greatest reduction in physical activities and social contacts occurred after testing.

Interpretation

These findings highlight the delayed response of patients in seeking medical attention and reducing social contacts and demonstrate the transformative potential of smartwatches for identifying infection and enabling timely public health interventions.

Funding

This work was supported by the European Research Council, project #949850, the Israel Science Foundation (ISF), grant No. 3409/19, within the Israel Precision Medicine Partnership program, and a Koret Foundation gift for Smart Cities and Digital Living.

Host behaviour driven by awareness of infection risk amplifies the chance of superspreading events

We demonstrate that heterogeneity in the perceived risks associated with infection within host populations amplifies chances of superspreading during the crucial early stages of epidemics. Under this behavioural model, individuals less concerned about dangers from infection are more likely to be infected and attend larger sized (riskier) events, where we assume event sizes remain unchanged. For directly transmitted diseases such as COVID-19, this leads to infections being introduced at rates above the population prevalence to those events most conducive to superspreading. We develop an interpretable, computational framework for evaluating within-event risks and derive a small-scale reproduction number measuring how the infections generated at an event depend on transmission heterogeneities and numbers of introductions. This generalizes previous frameworks and quantifies how event-scale patterns and population-level characteristics relate. As event duration and size grow, our reproduction number converges to the basic reproduction number. We illustrate that even moderate levels of heterogeneity in the perceived risks of infection substantially increase the likelihood of disproportionately large clusters of infections occurring at larger events, despite fixed overall disease prevalence. We show why collecting data linking host behaviour and event attendance is essential for accurately assessing the risks posed by invading pathogens in emerging stages of outbreaks.

Non-pharmaceutical interventions in containing COVID-19 pandemic after the roll-out of coronavirus vaccines: a systematic review

BACKGROUND

Non-pharmaceutical interventions (NPIs) have been widely utilised to control the COVID-19 pandemic. However, it is unclear what the optimal strategies are for implementing NPIs in the context of coronavirus vaccines. This study aims to systematically identify, describe, and evaluate existing ecological studies on the real-world impact of NPIs in containing COVID-19 pandemic following the roll-out of coronavirus vaccines.

METHODS

We conducted a comprehensive search of relevant studies from January 1, 2021, to June 4, 2023 in PubMed, Embase, Web of science and MedRxiv. Two authors independently assessed the eligibility of the studies and extracted the data. A risk of bias assessment tool, derived from a bibliometric review of ecological studies, was applied to evaluate the study design, statistical methodology, and the quality of reporting. Data were collected, synthesised and analysed using qualitative and quantitative methods. The results were presented using summary tables and figures, including information on the target countries and regions of the studies, types of NPIs, and the quality of evidence.

RESULTS

The review included a total of 17 studies that examined the real-world impact of NPIs in containing the COVID-19 pandemic after the vaccine roll-out. These studies used five composite indicators that combined multiple NPIs, and examined 14 individual NPIs. The studies had an average quality assessment score of 13 (range: 10-16), indicating moderately high quality. NPIs had a larger impact than vaccination in mitigating the spread of COVID-19 during the early stage of the vaccination implementation and in the context of the Omicron variant. Testing policies, workplace closures, and restrictions on gatherings were the most effective NPIs in containing the COVID-19 pandemic, following the roll-out of vaccines. The impact of NPIs varied across different time frames, countries and regions.

CONCLUSION

NPIs had a larger contribution to the control of the pandemic as compared to vaccination during the early stage of vaccine implementation and in the context of the omicron variant. The impact of NPIs in containing the COVID-19 pandemic exhibited variability in diverse contexts. Policy- and decision-makers need to focus on the impact of different NPIs in diverse contexts. Further research is needed to understand the policy mechanisms and address potential future challenges.

Heterogeneous risk tolerance, in-groups, and epidemic waves

There is a growing interest in the joint modeling of the dynamics of disease and health-related beliefs and attitudes, but coupling mechanisms are yet to be understood. We introduce a model where risk information, which can be delayed, comes in two flavors, including historical risk derived from perceived incidence data and predicted risk information. Our model also includes an interpretation domain where the behavioral response to risk information is subject to in-group pressure. We then simulate how the strength of behavioral reaction impacts epidemic severity as measured by epidemic peak size, number of waves, and final size. Simulated behavioral response is not effective when the level of protection that prophylactic behavior provides is as small as 50% or lower. At a higher level of 75% or more, we see the emergence of multiple epidemic waves. In addition, simulations show that different behavioral response profiles can lead to various epidemic outcomes that are non-monotonic with the strength of reaction to risk information. We also modeled heterogeneity in the response profile of a population and find they can lead to less severe epidemic outcome in terms of peak size.

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.

Mathematical model of voluntary vaccination against schistosomiasis

Human schistosomiasis is a chronic and debilitating neglected tropical disease caused by parasitic worms of the genus Schistosoma. It is endemic in many countries in sub-Saharan Africa. Although there is currently no vaccine available, vaccines are in development. In this paper, we extend a simple compartmental model of schistosomiasis transmission by incorporating the vaccination option. Unlike previous models of schistosomiasis transmission that focus on control and treatment at the population level, our model focuses on incorporating human behavior and voluntary individual vaccination. We identify vaccination rates needed to achieve herd immunity as well as optimal voluntary vaccination rates. We demonstrate that the prevalence remains too high (higher than 1%) unless the vaccination costs are sufficiently low. Thus, we can conclude that voluntary vaccination (with or without mass drug administration) may not be sufficient to eliminate schistosomiasis as a public health concern. The cost of the vaccine (relative to the cost of schistosomiasis infection) is the most important factor determining whether voluntary vaccination can yield elimination of schistosomiasis. When the cost is low, the optimal voluntary vaccination rate is high enough that the prevalence of schistosomiasis declines under 1%. Once the vaccine becomes available for public use, it will be crucial to ensure that the individuals have as cheap an access to the vaccine as possible.

Health protective behaviours during the COVID-19 pandemic: Risk adaptation or habituation?

Many epidemiological works show that human behaviours play a fundamental role in the spread of infectious diseases. However, we still do not know much about how people modify their Health Protective Behaviours (HPB), such as hygiene or social distancing measures, over time in response to the health threat during an epidemic. In this study, we examined the role of the epidemiological context in engagement in HPB through two possible mechanisms highlighted by research into decision-making under risk: risk adaptation and risk habituation. These two different mechanisms were assumed to explain to a large extent the temporal variations in the public's responsiveness to the health threat during the COVID-19 pandemic. To test them, we used self-reported data collected through a series of 25 cross-sectional surveys conducted in France among representative samples of the adult population, from March 2020 to September 2021 (N = 50,019). Interestingly, we found that both mechanisms accounted relatively well for the temporal variation in the adoption of social distancing during the pandemic, which is remarkable given their different assumptions about the underlying social cognitive processes involved in response to a health threat. These results suggest that strengthening the incentives to encourage people to maintain health protective behaviours and to counter risk habituation effects is crucial to disease control and prevention over time.

The unequal effects of the health-economy trade-off during the COVID-19 pandemic

Despite the global impact of the coronavirus disease 2019 pandemic, the question of whether mandated interventions have similar economic and public health effects as spontaneous behavioural change remains unresolved. Addressing this question, and understanding differential effects across socioeconomic groups, requires building quantitative and fine-grained mechanistic models. Here we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations and income levels. We validate the model by reproducing key outcomes of the first wave of coronavirus disease 2019 in the New York metropolitan area. The key mechanism coupling the epidemic and economic modules is the reduction in consumption due to fear of infection. In counterfactual experiments, we show that a similar trade-off between epidemic and economic outcomes exists both when individuals change their behaviour due to fear of infection and when non-pharmaceutical interventions are imposed. Low-income workers, who perform in-person occupations in customer-facing industries, face the strongest trade-off.

Disease avoidance threatens social cohesion in a large-scale social networking experiment

People tend to limit social contacts during times of increased health risks, leading to disruption of social networks thus changing the course of epidemics. To what extent, however, do people show such avoidance reactions? To test the predictions and assumptions of an agent-based model on the feedback loop between avoidance behavior, social networks, and disease spread, we conducted a large-scale (2,879 participants) incentivized experiment. The experiment rewards maintaining social relations and structures, and penalizes acquiring infections. We find that disease avoidance dominates networking decisions, despite relatively low penalties for infections; and that participants use more sophisticated strategies than expected (e.g., avoiding susceptible others with infectious neighbors), while they forget to maintain a beneficial network structure. Consequently, we observe low infection numbers, but also deterioration of network positions. These results imply that the focus on a more obvious signal (i.e., infection) may lead to unwanted side effects (i.e., loss of social cohesion).

Epidemic Spreading in Metapopulation Networks Coupled With Awareness Propagation

Understanding the feedback loop that links the spatiotemporal spread of infectious diseases and human behavior is an open problem. To study this problem, we develop a multiplex framework that couples epidemic spreading across subpopulations in a metapopulation network (i.e., physical layer) with the spreading of awareness about the epidemic in a communication network (i.e., virtual layer). We explicitly study the interactions between the mobility patterns across subpopulations and the awareness propagation among individuals. We analyze the coupled dynamics using microscopic Markov chains (MMCs) equations and validate the theoretical results via Monte Carlo (MC) simulations. We find that with the spreading of awareness, reducing human mobility becomes more effective in mitigating the large-scale epidemic. We also investigate the influence of varying topological features of the physical and virtual layers and the correlation between the connectivity and local population size per subpopulation. Overall the proposed modeling framework and findings contribute to the growing literature investigating the interplay between the spatiotemporal spread of epidemics and human behavior.

Impact of voluntary testing on infectious disease epidemiology: A game theoretic approach

The World Health Organization recommends test-and-treat interventions to curb and even eliminate epidemics of HIV, viral hepatitis, and sexually transmitted infections (e.g., chlamydia, gonorrhea, syphilis and trichomoniasis). Epidemic models show these goals are achievable, provided the participation of individuals in test-and-treat interventions is sufficiently high. We combine epidemic models and game theoretic models to describe individual's decisions to get tested for infectious diseases within certain epidemiological contexts, and, implicitly, their voluntary participation to test-and-treat interventions. We develop three hybrid models, to discuss interventions against HIV, HCV, and sexually transmitted infections, and the potential behavioral response from the target population. Our findings are similar across diseases. Particularly, individuals use three distinct behavioral patterns relative to testing, based on their perceived costs for testing, besides the payoff for discovering their disease status. Firstly, if the cost of testing is too high, then individuals refrain from voluntary testing and get tested only if they are symptomatic. Secondly, if the cost is moderate, some individuals will test voluntarily, starting treatment if needed. Hence, the spread of the disease declines and the disease epidemiology is mitigated. Thirdly, the most beneficial testing behavior takes place as individuals perceive a per-test payoff that surpasses a certain threshold, every time they get tested. Consequently, individuals achieve high voluntary testing rates, which may result in the elimination of the epidemic, albeit on temporary basis. Trials and studies have attained different levels of participation and testing rates. To increase testing rates, they should provide each eligible individual with a payoff, above a given threshold, each time the individual tests voluntarily.

Internal and external factors affecting vaccination coverage: Modeling the interactions between vaccine hesitancy, accessibility, and mandates

Society, culture, and individual motivations affect human decisions regarding their health behaviors and preventative care, and health-related perceptions and behaviors can change at the population level as cultures evolve. An increase in vaccine hesitancy, an individual mindset informed within a cultural context, has resulted in a decrease in vaccination coverage and an increase in vaccine-preventable disease (VPD) outbreaks, particularly in developed countries where vaccination rates are generally high. Understanding local vaccination cultures, which evolve through an interaction between beliefs and behaviors and are influenced by the broader cultural landscape, is critical to fostering public health. Vaccine mandates and vaccine inaccessibility are two external factors that interact with individual beliefs to affect vaccine-related behaviors. To better understand the population dynamics of vaccine hesitancy, it is important to study how these external factors could shape a population's vaccination decisions and affect the broader health culture. Using a mathematical model of cultural evolution, we explore the effects of vaccine mandates, vaccine inaccessibility, and varying cultural selection trajectories on a population's level of vaccine hesitancy and vaccination behavior. We show that vaccine mandates can lead to a phenomenon in which high vaccine hesitancy co-occurs with high vaccination coverage, and that high vaccine confidence can be maintained even in areas where access to vaccines is limited.

Rational social distancing policy during epidemics with limited healthcare capacity

Epidemics of infectious diseases posing a serious risk to human health have occurred throughout history. During recent epidemics there has been much debate about policy, including how and when to impose restrictions on behaviour. Policymakers must balance a complex spectrum of objectives, suggesting a need for quantitative tools. Whether health services might be 'overwhelmed' has emerged as a key consideration. Here we show how costly interventions, such as taxes or subsidies on behaviour, can be used to exactly align individuals' decision making with government preferences even when these are not aligned. In order to achieve this, we develop a nested optimisation algorithm of both the government intervention strategy and the resulting equilibrium behaviour of individuals. We focus on a situation in which the capacity of the healthcare system to treat patients is limited and identify conditions under which the disease dynamics respect the capacity limit. We find an extremely sharp drop in peak infections at a critical maximum infection cost in the government's objective function. This is in marked contrast to the gradual reduction of infections if individuals make decisions without government intervention. We find optimal interventions vary less strongly in time when interventions are costly to the government and that the critical cost of the policy switch depends on how costly interventions are.

Rational social distancing in epidemics with uncertain vaccination timing

During epidemics people may reduce their social and economic activity to lower their risk of infection. Such social distancing strategies will depend on information about the course of the epidemic but also on when they expect the epidemic to end, for instance due to vaccination. Typically it is difficult to make optimal decisions, because the available information is incomplete and uncertain. Here, we show how optimal decision-making depends on information about vaccination timing in a differential game in which individual decision-making gives rise to Nash equilibria, and the arrival of the vaccine is described by a probability distribution. We predict stronger social distancing the earlier the vaccination is expected and also the more sharply peaked its probability distribution. In particular, equilibrium social distancing only meaningfully deviates from the no-vaccination equilibrium course if the vaccine is expected to arrive before the epidemic would have run its course. We demonstrate how the probability distribution of the vaccination time acts as a generalised form of discounting, with the special case of an exponential vaccination time distribution directly corresponding to regular exponential discounting.

Guidance on the use of complex systems models for economic evaluations of public health interventions

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

Modeling the "F" in "SAFE": The dynamic game of facial cleanliness in trachoma prevention

Trachoma, a neglected tropical disease (NTDs) caused by bacterium Chlamydia trachomatis, is a leading cause of infectious blindness. Efforts are underway to eliminate trachoma as a public health problem by using the "SAFE" strategy. While mathematical models are now standard tools used to support elimination efforts and there are a variety of models studying different aspects of trachoma transmission dynamics, the "F" component of the strategy corresponding to facial cleanliness has received very little attention so far. In this paper, we incorporate human behavior into a standard epidemiological model and develop a dynamical game during which individuals practice facial cleanliness based on their epidemiological status and perceived benefits and costs. We found that the number of infectious individuals generally increases with the difficulty to access a water source. However, this increase happens only during three transition periods and the prevalence stays constant otherwise. Consequently, improving access to water can help eliminate trachoma, but the improvement needs to be significant enough to cross at least one of the three transition thresholds; otherwise the improved access will have no noticeable effect.

A 10-year prospectus for mathematical epidemiology

There is little significant work at the intersection of mathematical and computational epidemiology and detailed psychological processes, representations, and mechanisms. This is true despite general agreement in the scientific community and the general public that human behavior in its seemingly infinite variation and heterogeneity, susceptibility to bias, context, and habit is an integral if not fundamental component of what drives the dynamics of infectious disease. The COVID-19 pandemic serves as a close and poignant reminder. We offer a 10-year prospectus of kinds that centers around an unprecedented scientific approach: the integration of detailed psychological models into rigorous mathematical and computational epidemiological frameworks in a way that pushes the boundaries of both psychological science and population models of behavior.

Modeling the effects of Prophylactic behaviors on the spread of SARS-CoV-2 in West Africa

Various general and individual measures have been implemented to limit the spread of SARS-CoV-2 since its emergence in China. Several phenomenological and mechanistic models have been developed to inform and guide health policy. Many of these models ignore opinions about certain control measures, although various opinions and attitudes can influence individual actions. To account for the effects of prophylactic opinions on disease dynamics and to avoid identifiability problems, we expand the SIR-Opinion model of Tyson et al. (2020) to take into account the partial detection of infected individuals in order to provide robust modeling of COVID-19 as well as degrees of adherence to prophylactic treatments, taking into account a hybrid modeling technique using Richard's model and the logistic model. Applying the approach to COVID-19 data from West Africa demonstrates that the more people with a strong prophylactic opinion, the smaller the final COVID-19 pandemic size. The influence of individuals on each other and from the media significantly influences the susceptible population and, thus, the dynamics of the disease. Thus, when considering the opinion of susceptible individuals to the disease, the view of the population at baseline influences its dynamics. The results are expected to inform public policy in the context of emerging and re-emerging infectious diseases.

Coronavirus-Related Searches on the Internet Predict COVID-19 Vaccination Rates in the Real World: A Behavioral Immune System Perspective

According to the smoke detector and functional flexibility principles of human behavioral immune system (BIS), the exposure to COVID-19 cues could motivate vaccine uptake. Using the tool of Google Trends, we tested that coronavirus-related searches-which assessed natural exposure to COVID-19 cues-would positively predict actual vaccination rates. As expected, coronavirus-related searches positively and significantly predicted vaccination rates in the United States (Study 1a) and across the globe (Study 2a) after accounting for a range of covariates. The stationary time series analyses with covariates and autocorrelation structure of the dependent variable confirmed that more coronavirus-related searches compared with last week indicated increases in vaccination rates compared with last week in the United States (Study 1b) and across the globe (Study 2b). With real-time web search data, psychological scientists could test their research questions in real-life settings and at a large scale to expand the ecological validity and generalizability of the findings.

A minimal model for adaptive SIS epidemics

The interplay between disease spreading and personal risk perception is of key importance for modelling the spread of infectious diseases. We propose a planar system of ordinary differential equations (ODEs) to describe the co-evolution of a spreading phenomenon and the average link density in the personal contact network. Contrary to standard epidemic models, we assume that the contact network changes based on the current prevalence of the disease in the population, i.e. the network adapts to the current state of the epidemic. We assume that personal risk perception is described using two functional responses: one for link-breaking and one for link-creation. The focus is on applying the model to epidemics, but we also highlight other possible fields of application. We derive an explicit form for the basic reproduction number and guarantee the existence of at least one endemic equilibrium, for all possible functional responses. Moreover, we show that for all functional responses, limit cycles do not exist. This means that our minimal model is not able to reproduce consequent waves of an epidemic, and more complex disease or behavioural dynamics are required to reproduce epidemic waves.

A cultural evolutionary model of the interaction between parental beliefs and behaviors, with applications to vaccine hesitancy

Health perceptions and health-related behaviors can change at the population level as cultures evolve. In the last decade, despite the proven efficacy of vaccines, the developed world has seen a resurgence of vaccine-preventable diseases (VPDs) such as measles, pertussis, and polio. Vaccine hesitancy, an individual attitude influenced by historical, political, and socio-cultural forces, is believed to be a primary factor responsible for decreasing vaccine coverage, thereby increasing the risk and occurrence of VPD outbreaks. Behavior change models have been increasingly employed to understand disease dynamics and intervention effectiveness. However, since health behaviors are culturally influenced, it is valuable to examine them within a cultural evolution context. Here, using a mathematical modeling framework, we explore the effects of cultural evolution on vaccine hesitancy and vaccination behavior. With this model, we shed light on facets of cultural evolution (vertical transmission, community influences, homophily, etc.) that promote the spread of vaccine hesitancy, ultimately affecting levels of vaccination coverage and VPD outbreak risk in a population. In addition, we present our model as a generalizable framework for exploring cultural evolution when humans' beliefs influence, but do not strictly dictate, their behaviors. This model offers a means of exploring how parents' potentially conflicting beliefs and cultural traits could affect their children's health and fitness. We show that vaccine confidence and vaccine-conferred benefits can both be driving forces of vaccine coverage. We also demonstrate that an assortative preference among vaccine-hesitant individuals can lead to increased vaccine hesitancy and lower vaccine coverage.

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.

Spatiotemporal Trends in Self-Reported Mask-Wearing Behavior in the United States: Analysis of a Large Cross-sectional Survey

BACKGROUND

Face mask wearing has been identified as an effective strategy to prevent the transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance, potentially generating heterogeneities in the local trajectories of COVID-19 in the United States. Although numerous studies have investigated the patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask wearing at fine spatial scales across the United States through different phases of the pandemic.

OBJECTIVE

Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the United States. This information is critical to further assess the effectiveness of masking, evaluate the drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges.

METHODS

We analyzed spatiotemporal masking patterns in over 8 million behavioral survey responses from across the United States, starting in September 2020 through May 2021. We adjusted for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debiased self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county level. Lastly, we evaluated whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data.

RESULTS

We found that county-level masking behavior was spatially heterogeneous along an urban-rural gradient, with mask wearing peaking in winter 2021 and declining sharply through May 2021. Our results identified regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask wearing may be influenced by national guidance and disease prevalence. We validated our bias correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of a small sample size and representation. Self-reported behavior estimates were especially prone to social desirability and nonresponse biases, and our findings demonstrated that these biases can be reduced if individuals are asked to report on community rather than self behaviors.

CONCLUSIONS

Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.

Elasticity and substitutability of food demand and emerging disease risk on livestock farms

Disease emergence in livestock is a product of environment, epidemiology and economic forces. The environmental factors contributing to novel pathogen emergence in humans have been studied extensively, but the two-way relationship between farm microeconomics and outbreak risk has received comparably little attention. We introduce a game-theoretic model where farmers produce and sell two goods, one of which (e.g. pigs, poultry) is susceptible to infection by a pathogen. We model market and epidemiological effects at both the individual farm level and the community level. We find that in the case of low demand elasticity for livestock meat, the presence of an animal pathogen causing production losses can lead to a bistable system where two outcomes are possible: (i) successful disease control or (ii) maintained disease circulation, where farmers slaughter their animals at a low rate, face substantial production losses, but maintain large herds because of the appeal of high meat prices. Our observations point to the potentially critical effect of price elasticity of demand for livestock products on the success or failure of livestock disease control policies. We show the potential epidemiological benefits of (i) policies aimed at stabilizing livestock product prices, (ii) subsidies for alternative agricultural activities during epidemics, and (iii) diversifying agricultural production and sources of proteins available to consumers.

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.

A model of behavioural response to risk accurately predicts the statistical distribution of COVID-19 infection and reproduction numbers

One clear aspect of behaviour in the COVID-19 pandemic has been people's focus on, and response to, reported or observed infection numbers in their community. We describe a simple model of infectious disease spread in a pandemic situation where people's behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. This homeostatic response is active until approximate herd immunity is reached: in this domain the model predicts that the reproduction rate R will be centred around a median of 1, that proportional change in infection numbers will follow the standard Cauchy distribution with location and scale parameters 0 and 1, and that high infection numbers will follow a power-law frequency distribution with exponent 2. To test these predictions we used worldwide COVID-19 data from 1st February 2020 to 30th June 2022 to calculate [Formula: see text] confidence interval estimates across countries for these R, location, scale and exponent parameters. The resulting median R estimate was [Formula: see text] (predicted value 1) the proportional change location estimate was [Formula: see text] (predicted value 0), the proportional change scale estimate was [Formula: see text] (predicted value 1), and the frequency distribution exponent estimate was [Formula: see text] (predicted value 2); in each case the observed estimate agreed with model predictions.

Imperfect vaccine can yield multiple Nash equilibria in vaccination games

As infectious diseases continue to threaten communities across the globe, people are faced with a choice to vaccinate, or not. Many factors influence this decision, such as the cost of the disease, the chance of contracting the disease, the population vaccination coverage, and the efficacy of the vaccine. While the vaccination games in which individuals decide whether to vaccinate or not based on their own interests are gaining in popularity in recent years, the vaccine imperfection has been an overlooked aspect so far. In this paper we investigate the effects of an imperfect vaccine on the outcomes of a vaccination game. We use a simple SIR compartmental model for the underlying model of disease transmission. We model the vaccine imperfection by adding vaccination at birth and maintain a possibility for the vaccinated individual to become infected. We derive explicit conditions for the existence of different Nash equilibria, the solutions of the vaccination game. The outcomes of the game depend on the complex interplay between disease transmission dynamics (the basic reproduction number), the relative cost of the infection, and the vaccine efficacy. We show that for diseases with relatively low basic reproduction numbers (smaller than about 2.62), there is a little difference between outcomes for perfect or imperfect vaccines and thus the simpler models assuming perfect vaccines are good enough. However, when the basic reproduction number is above 2.62, then, unlike in the case of a perfect vaccine, there can be multiple equilibria. Moreover, unless there is a mandatory vaccination policy in place that would push the vaccination coverage above the value of unstable Nash equilibrium, the population could eventually slip to the "do not vaccinate" state. Thus, for diseases that have relatively high basic reproduction numbers, the potential for the vaccine not being perfect should be explicitly considered in the models.

Unraveling the dynamics of the Omicron and Delta variants of the 2019 coronavirus in the presence of vaccination, mask usage, and antiviral treatment

The effectiveness of control interventions against COVID-19 is threatened by the emergence of SARS-CoV-2 variants of concern. We present a mathematical model for studying the transmission dynamics of two of these variants (Delta and Omicron) in the United States, in the presence of vaccination, treatment of individuals with clinical symptoms of the disease and the use of face masks. The model is parameterized and cross-validated using observed daily case data for COVID-19 in the United States for the period from November 2021 (when Omicron first emerged) to March 2022. Rigorous qualitative analysis of the model shows that the disease-free equilibrium of the model is locally-asymptotically stable when the control reproduction number of the model (denoted by R c ) is less than one. This equilibrium is shown to be globally-asymptotically stable for a special case of the model, where disease-induced mortality is negligible and both vaccine-derived immunity in fully-vaccinated individuals and natural immunity do not wane, when the associated reproduction number is less than one. The epidemiological implication of the latter result is that the combined vaccination-boosting strategy can lead to the elimination of the pandemic if its implementation can bring (and maintain) the associated reproduction number to a value less than one. An analytical expression for the vaccine-derived herd immunity threshold is derived. Using this expression, together with the baseline values of the parameters of the parameterized model, we showed that the vaccine-derived herd immunity can be achieved in the United States (so that the pandemic will be eliminated) if at least 68 % of the population is fully-vaccinated with two of the three vaccines approved for use in the United States (Pfizer or Moderna vaccine). Furthermore, this study showed (as of the time of writing in March 2022) that the control reproduction number of the Omicron variant was approximately 3.5 times that of the Delta variant (the reproduction of the latter is computed to be ≈ 0.2782 ), indicating that Delta had practically died out and that Omicron has competitively-excluded Delta (to become the predominant variant in the United States). Based on our analysis and parameterization at the time of writing of this paper (March 2022), our study suggests that SARS-CoV-2 elimination is feasible by June 2022 if the current baseline level of the coverage of fully-vaccinated individuals is increased by about 20 % . The prospect of pandemic elimination is significantly improved if vaccination is combined with a face mask strategy that prioritizes moderately effective and high-quality masks. Having a high percentage of the populace wearing the moderately-effective surgical mask is more beneficial to the community than having low percentage of the populace wearing the highly-effective N95 masks. We showed that waning natural and vaccine-derived immunity (if considered individually) offer marginal impact on disease burden, except for the case when they wane at a much faster rate (e.g., within three months), in comparison to the baseline (estimated to be within 9 months to a year). Treatment of symptomatic individuals has marginal effect in reducing daily cases of SARS-CoV-2, in comparison to the baseline, but it has significant impact in reducing daily hospitalizations. Furthermore, while treatment significantly reduces daily hospitalizations (and, consequently, deaths), the prospects of COVID-19 elimination in the United States are significantly enhanced if investments in control resources are focused on mask usage and vaccination rather than on treatment.

A multiscale modeling framework to study the interdependence of brain, behavior, and pandemic

A major constraint of the behavioral epidemiological models is the assumption that human behavior is static; however, it is highly dynamic, especially in uncertain circumstances during a pandemic. To incorporate the dynamicity of human nature in the existing epidemiological models, we propose a population-wide multi-time-scale theoretical framework that assimilates neuronal plasticity as the basis of altering human emotions and behavior. For that, variable connection weights between different brain regions and their firing frequencies are coupled with a compartmental susceptible-infected-recovered model to incorporate the intrinsic dynamicity in the contact transmission rate ( β ). As an illustration, a model of fear conditioning in conjunction with awareness campaigns is developed and simulated. Results indicate that in the presence of fear conditioning, there exists an optimum duration of daily broadcast time during which awareness campaigns are most effective in mitigating the pandemic. Further, global sensitivity analysis using the Morris method highlighted that the learning rate and firing frequency of the unconditioned circuit are crucial regulators in modulating the emergent pandemic waves. The present study makes a case for incorporating neuronal dynamics as a basis of behavioral immune response and has further implications in designing awareness campaigns.

A computational cognitive model of behaviors and decisions that modulate pandemic transmission: Expectancy-value, attitudes, self-efficacy, and motivational intensity

We present a computational cognitive model that incorporates and formalizes aspects of theories of individual-level behavior change and present simulations of COVID-19 behavioral response that modulates transmission rates. This formalization includes addressing the psychological constructs of attitudes, self-efficacy, and motivational intensity. The model yields signature phenomena that appear in the oscillating dynamics of mask wearing and the effective reproduction number, as well as the overall increase of rates of mask-wearing in response to awareness of an ongoing pandemic.

Spatiotemporal trends in self-reported mask-wearing behavior in the United States: Analysis of a large cross-sectional survey

Background

Face mask-wearing has been identified as an effective strategy to prevent transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance potentially generating heterogeneities in the local trajectories of COVID-19 in the U.S. While numerous studies have investigated patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask-wearing at fine spatial scales across the U.S. through different phases of the pandemic.

Objective

Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the U.S. This information is critical to further assess the effectiveness of masking, evaluate drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges.

Methods

We analyze spatiotemporal masking patterns in over eight million behavioral survey responses from across the United States starting in September 2020 through May 2021. We adjust for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debias self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county-level. Lastly, we evaluate whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data.

Results

We find that county-level masking behavior is spatially heterogeneous along an urban-rural gradient, with mask-wearing peaking in winter 2021 and declining sharply through May 2021. Our results identify regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask-wearing may be influenced by national guidance and disease prevalence. We validate our bias-correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of small sample size and representation. Self-reported behavior estimates are especially prone to social desirability and non-response biases and our findings demonstrate that these biases can be reduced if individuals are asked to report on community rather than self behaviors.

Conclusions

Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.

Optimal control by deep learning techniques and its applications on epidemic models

We represent the optimal control functions by neural networks and solve optimal control problems by deep learning techniques. Adjoint sensitivity analysis is applied to train the neural networks embedded in differential equations. This method can not only be applied in classic epidemic control problems, but also in epidemic forecasting, discovering unknown mechanisms, and the ideas behind can give new insights to traditional mathematical epidemiological problems.