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1. Heterogeneous adaptive behavioral responses may increase epidemic burden

   https://doi.org/10.1038/s41598-022-15444-8  

   Espinoza, Baltazar; Swarup, Samarth; Barrett, Christopher L.; Marathe, Madhav (December 2022, Scientific Reports)

   Abstract Non-pharmaceutical interventions (NPIs) constitute the front-line responses against epidemics. Yet, the interdependence of control measures and individual microeconomics, beliefs, perceptions and health incentives, is not well understood. Epidemics constitute complex adaptive systems where individual behavioral decisions drive and are driven by, among other things, the risk of infection. To study the impact of heterogeneous behavioral responses on the epidemic burden, we formulate a two risk-groups mathematical model that incorporates individual behavioral decisions driven by risk perceptions. Our results show a trade-off between the efforts to avoid infection by the risk-evader population, and the proportion of risk-taker individuals with relaxed infection risk perceptions. We show that, in a structured population, privately computed optimal behavioral responses may lead to an increase in the final size of the epidemic, when compared to the homogeneous behavior scenario. Moreover, we find that uncertain information on the individuals’ true health state may lead to worse epidemic outcomes, ultimately depending on the population’s risk-group composition. Finally, we find there is a set of specific optimal planning horizons minimizing the final epidemic size, which depend on the population structure. 

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2. A Cost-Sensitive Behavioral Modeling Analysis of the Early Identification and Control of Infectious Diseases

   https://doi.org/10.1007/s11538-026-01696-2  

   Ullah, Mohammad Sharif; Wang, Xiunan; Wang, Jin (June 2026, Bulletin of Mathematical Biology)

   A coupled epidemic-behavior model is proposed to assess how vaccination, testing, and treatment jointly influence early outbreak control, accounting for individual choices based on perceived risk, intervention costs, and policy incentives. In a uniformly mixing population, individuals make strategic decisions regarding vaccination and testing participation based on the trade-offs between personal costs and communal benefits, while the intensity of public health testing adjusts according to infection prevalence; the transmission model includes vaccine efficacy, the probability of false negatives in testing, treatment-related reductions in infectious duration and mortality, as well as nonlinear saturation and awareness feedback mechanisms. Analytical threshold results and simulations (time series and two-dimensional parameter sweeps) indicate that reducing vaccination and testing costs expands the disease-free region, while false negatives consistently diminish the efficacy of increased testing by delaying diagnosis and perpetuating onward transmission; treatment reduces transmission potential but maximizes population benefit when combined with prompt implementation of testing. The approach also delineates parameter regimes in which behavioral feedback provides non-monotonic responses and recurring outbreaks, notwithstanding heightened interventions. The findings suggest that effective epidemic preparedness necessitates synchronized incentives for vaccination and testing, investments to minimize false negatives (such as enhanced test accuracy or repeated testing), and treatment accessibility aligned with rapid case identification to optimize resource distribution and early-response strategies during emerging and re-emerging infectious disease threats. 

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3. 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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4. Social divisions and risk perception drive divergent epidemics and large later waves

   https://doi.org/10.1017/ehs.2023.2  

   Harris, Mallory J.; Cardenas, Kimberly J.; Mordecai, Erin A. (January 2023, Evolutionary Human Sciences)

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

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5. 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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