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1. Adaptive human behavior and delays in information availability autonomously modulate epidemic waves

   https://doi.org/10.1093/pnasnexus/pgaf145  

   Mahmud, Md Shahriar; Eshun, Solomon; Espinoza, Baltazar; Kadelka, Claus (April 2025, PNAS Nexus)

   Arenas, Alex (Ed.)

   Abstract The recurrence of epidemic waves has been a hallmark of infectious disease outbreaks. Repeated surges in infections pose significant challenges to public health systems, yet the mechanisms that drive these waves remain insufficiently understood. Most prior models attribute epidemic waves to exogenous factors, such as transmission seasonality, viral mutations, or implementation of public health interventions. We show that epidemic waves can emerge autonomously from the feedback loop between infection dynamics and human behavior. Our results are based on a behavioral framework in which individuals continuously adjust their level of risk mitigation subject to their perceived risk of infection, which depends on information availability and disease severity. We show that delayed behavioral responses alone can lead to the emergence of multiple epidemic waves. The magnitude and frequency of these waves depend on the interplay between behavioral factors (delay, severity, and sensitivity of responses) and disease factors (transmission and recovery rates). Notably, if the response is either too prompt or excessively delayed, multiple waves cannot emerge. Our results further align with previous observations that adaptive human behavior can produce nonmonotonic final epidemic sizes, shaped by the trade-offs between various biological and behavioral factors—namely, risk sensitivity, response stringency, and disease generation time. Interestingly, we found that the minimal final epidemic size occurs on regimes that exhibit a few damped oscillations. Altogether, our results emphasize the importance of integrating social and operational factors into infectious disease models, in order to capture the joint evolution of adaptive behavioral responses and epidemic dynamics. 

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

   https://doi.org/10.1016/j.mbs.2024.109368  

   Osi, Ann; Ghaffarzadegan, Navid (February 2025, Mathematical Biosciences)

   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. 

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3. The Impact of Risk Perception on Policy Effectiveness With a Cognitive Epidemiological Model

   https://doi.org/10.2139/ssrn.7283578  

   Freedman, Ari; Gross, Louis; Lenhart, Suzanne; Lacasse, Katherine; Saad-Roy, Chadi M; Sims, Charles; Beckage, Brian (January 2026, SSRN)

   Incorporating human behavior into epidemiology models is increasingly realized as a useful way to assess the effectiveness of public health policies. Due to limited agreement on how individuals perceive, process, and respond to risk, standard disease models often implicitly assume individuals rationally respond to disease risk and accurately process information about the risks they face, neglecting important psychological processes. We present a cognitive epidemiological model framework supplemented with state variables representing human risk perception and behavior to explore how risk perception influences epidemiological and behavioral dynamics, and how these processes interact with public health policy strategies. We consider public policies which target top-down effects (directly mandating epidemic-controlling actions) or bottom-up effects (encouraging change by influencing people's behavior and risk perception), or consist of multiple policy effects simultaneously. Top-down policies may have unintentional indirect bottom-up effects, which we show can lead to surprising interactions in how top-down and bottom-up policy actions combine to shape epidemics. Our model shows that different policy types produce distinct epidemiological and behavioral dynamics, with policies targeting bottom-up change better able to delay epidemic peaks but at the cost of greater vulnerability to the detrimental effects of mistimed interventions and people free-riding off others' protective behaviors. These results emphasize the benefits of collaboration between epidemiologists and social scientists to parameterize the behavioral and cognitive aspects of models like ours, which can enhance predictions and provide more nuanced policy recommendations. 

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

   https://doi.org/10.1371/journal.pcbi.1011992  

   Osi, Ann; Ghaffarzadegan, Navid (March 2024, PLOS Computational Biology)

   Struchiner, Claudio José (Ed.)

   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. 

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5. Addressing Perceived Apathy Through Engaged Learning in Crisis Communication: Lessons From Bird Flu (H5N1) in Dairy

   https://doi.org/10.1111/1468-5973.70069  

   Soares, Rodrigo; Sellnow, Timothy L; Sellnow, Deanna D; Merrill, Scott C; Smith, Julia M; Frizzo, Martina (September 2025, Journal of Contingencies and Crisis Management)

   ABSTRACT Recent outbreaks of A/H5N1 (Highly Pathogenic Avian Influenza virus/HPAIv) —commonly referred to as bird flu virus—have mutated and spread beyond poultry to also infect dairy cattle and humans. Despite warnings by crisis communication practitioners affiliated with the dairy industry, the response is widely criticized as underwhelming and apathetic. This study proposes engaged learning as an instructional risk and crisis co strategy for overcoming apathetic crisis denial. More specifically, a Reflexive Thematic Analysis (RTA) was conducted based on 21 semi‐structured interviews with practitioners who work in direct or indirect contact with farmers, producers, and decision‐makers associated with the dairy cattle industry. Results revealed that study participants support employing engaged learning communication strategies to overcome apathetic crisis denial and achieve desired affective, cognitive, and behavioral learning outcomes. These strategies include engaged and ongoing dialogue focused on: (1) internalization strategies to co‐construct meaning about the relevance and impact of the risks to risk‐bearers (affective learning); (2) explanation strategies to co‐construct mutual understanding about the nature of the risks including how to prevent infection and control its spread (cognitive learning); and (3) acknowledge and address trust issues based on stakeholders' previous crisis experiences. In doing so, stakeholders may overcome apathetic crisis denial and choose to engage in recommended biosecurity practices (behavioral learning). 

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