More Like this

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. 

   more » « less
2. 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. 

   more » « less
3. 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. 

   more » « less
4. Understanding the coevolution of mask wearing and epidemics: A network perspective

   https://doi.org/10.1073/pnas.2123355119  

   Qiu, Zirou; Espinoza, Baltazar; Vasconcelos, Vitor V.; Chen, Chen; Constantino, Sara M.; Crabtree, Stefani A.; Yang, Luojun; Vullikanti, Anil; Chen, Jiangzhuo; Weibull, Jörgen; et al (June 2022, Proceedings of the National Academy of Sciences)

   Nonpharmaceutical interventions (NPIs) such as mask wearing can be effective in mitigating the spread of infectious diseases. Therefore, understanding the behavioral dynamics of NPIs is critical for characterizing the dynamics of disease spread. Nevertheless, standard infection models tend to focus only on disease states, overlooking the dynamics of “beneficial contagions,” e.g., compliance with NPIs. In this work, we investigate the concurrent spread of disease and mask-wearing behavior over multiplex networks. Our proposed framework captures both the competing and complementary relationships between the dueling contagion processes. Further, the model accounts for various behavioral mechanisms that influence mask wearing, such as peer pressure and fear of infection. Our results reveal that under the coupled disease–behavior dynamics, the attack rate of a disease—as a function of transition probability—exhibits a critical transition. Specifically, as the transmission probability exceeds a critical threshold, the attack rate decreases abruptly due to sustained mask-wearing responses. We empirically explore the causes of the critical transition and demonstrate the robustness of the observed phenomena. Our results highlight that without proper enforcement of NPIs, reductions in the disease transmission probability via other interventions may not be sufficient to reduce the final epidemic size. 

   more » « less
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. 

   more » « less