Replicator Dynamics and Behavior-Augmented Multiscale Epidemic Modeling

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.