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1. Simulating human behavioral changes in livestock production systems during an epidemic: The case of the US beef cattle industry

   https://doi.org/10.1371/journal.pone.0253498  

   Yang, Qihui; Gruenbacher, Don M.; Brase, Gary L.; Heier Stamm, Jessica L.; DeLoach, Scott A.; Scoglio, Caterina M. (June 2021, PLOS ONE)

   Woźniakowski, Grzegorz (Ed.)

   Human behavioral change around biosecurity in response to increased awareness of disease risks is a critical factor in modeling animal disease dynamics. Here, biosecurity is referred to as implementing control measures to decrease the chance of animal disease spreading. However, social dynamics are largely ignored in traditional livestock disease models. Not accounting for these dynamics may lead to substantial bias in the predicted epidemic trajectory. In this research, an agent-based model is developed by integrating the human decision-making process into epidemiological processes. We simulate human behavioral change on biosecurity practices following an increase in the regional disease incidence. We apply the model to beef cattle production systems in southwest Kansas, United States, to examine the impact of human behavior factors on a hypothetical foot-and-mouth disease outbreak. The simulation results indicate that heterogeneity of individuals regarding risk attitudes significantly affects the epidemic dynamics, and human-behavior factors need to be considered for improved epidemic forecasting. With the same initial biosecurity status, increasing the percentage of risk-averse producers in the total population using a targeted strategy can more effectively reduce the number of infected producer locations and cattle losses compared to a random strategy. In addition, the reduction in epidemic size caused by the shifting of producers’ risk attitudes towards risk-aversion is heavily dependent on the initial biosecurity level. A comprehensive investigation of the initial biosecurity status is recommended to inform risk communication strategy design. 

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2. Replicator dynamics and behavior-augmented multiscale epidemic modeling

   https://doi.org/10.1063/5.0297383  

   Wan, Jinming; Liu, Qingyue; Zacks, Kathryn; Zhao, Jia; Ichinose, Genki; Small, Michael; Cheng, Changqing (December 2025, Chaos: An Interdisciplinary Journal of Nonlinear Science)

   This study presents a multiscale modeling framework that integrates stochastic integrate-and-fire dynamics, networked replicator dynamics, and a behavior-augmented susceptible–exposed–infectious–recovered–susceptible model to simulate the interplay between agent behaviors and infection spread. Traditional compartmental epidemiological models often overlook behavioral adaptations and network complexity that underlie disease transmission. In contrast, our approach captures the stochastic acquired viral load, reflecting how repeated exposures increase the risk of infection, while dynamically modeling the agent's protective behaviors as they adapt to perceived risks, imitation influence, and aggregate costs. The integration of these components allows the model to explicitly capture synchronization between behavioral adaptation and infection prevalence. Simulations reveal that this synchronized feedback can give rise to recurrent outbreaks, in which declining risk perception triggers a resurgence. More specifically, the integrate-and-fire mechanism buffers abrupt transitions, smoothing epidemic waves, while information delays exacerbate fluctuations. However, elevated risk awareness mitigates these disruptions by sustaining proactive behavior. By bridging evolutionary game theory, network science, and epidemiological modeling, this framework demonstrates that epidemic control depends not only on biological parameters but also on the timing, adaptation, and synchronization of collective behavior. Our findings underscore three critical components for epidemic control: timely communication, risk-aware policies, and feedback-responsive interventions. These strategies can stabilize disease dynamics and improve preparedness for future pandemics. 

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3. 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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4. Toward World Models for Epidemiology

   Memon, Zeeshan; Su, Yiqi; Thomas, Christo Kurisummoottil; Saad, Walid; Zhao, Liang; Ramakrishnan, Naren (March 2026, International Conference on Learning Research 2026 the 2nd Workshop on World Models)

   World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertainty. In this paper, we argue that computational epidemiology is a natural and underdeveloped setting for world models. This is because epidemic decision-making requires reasoning about latent disease burden, imperfect and policy-dependent surveillance signals, and intervention effects are mediated by adaptive human behavior. We introduce a conceptual framework for epidemiological world models, formulating epidemics as controlled, partially observed dynamical systems in which (i) the true epidemic state is latent, (ii) observations are noisy and endogenous to policy, and (iii) interventions act as sequential actions whose effects propagate through behavioral and social feedback. We present three case studies that illustrate why explicit world modeling is necessary for policy-relevant reasoning: strategic misreporting in behavioral surveillance, systematic delays in time-lagged signals such as hospitalizations and deaths, and counterfactual intervention analysis where identical histories diverge under alternative action sequences. 

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5. Improving policy design and epidemic response using integrated models of economic choice and disease dynamics with behavioral feedback

   https://doi.org/10.1371/journal.pcbi.1013549  

   Du, Hongru; Zahn, Matthew V; Loo, Sara L; Alleman, Tijs W; Truelove, Shaun; Patenaude, Bryan; Gardner, Lauren M; Papageorge, Nicholas; Hill, Alison L (October 2025, PLOS Computational Biology)

   Geard, Nicholas (Ed.)

   Human behavior plays a crucial role in infectious disease transmission, yet traditional models often overlook or oversimplify this factor, limiting predictions of disease spread and the associated socioeconomic impacts. Here we introduce a feedback-informed epidemiological model that integrates human behavior with disease dynamics in a credible, tractable, and extendable manner. From economics, we incorporate a dynamic decision-making model where individuals assess the trade-off between disease risks and economic consequences, and then link this to a risk-stratified compartmental model of disease spread taken from epidemiology. In the unified framework, heterogeneous individuals make choices based on current and future payoffs, influencing their risk of infection and shaping population-level disease dynamics. As an example, we model disease-decision feedback during the early months of the COVID-19 pandemic, when the decision to participate in paid, in-person work was a major determinant of disease risk. Comparing the impacts of stylized policy options representing mandatory, incentivized/compensated, and voluntary work abstention, we find that accounting for disease-behavior feedback has a significant impact on the relative health and economic impacts of policies. Including two crucial dimensions of heterogeneity—health and economic vulnerability—the results highlight how inequities between risk groups can be exacerbated or alleviated by disease control measures. Importantly, we show that a policy of more stringent workplace testing can potentially slow virus spread and, surprisingly, increase labor supply since individuals otherwise inclined to remain at home to avoid infection perceive a safer workplace. In short, our framework permits the exploration of avenues whereby health and wealth need not always be at odds. This flexible and extendable modeling framework offers a powerful tool for understanding the interplay between human behavior and disease spread. 

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