(untitled) (CSIAM Transactions on Life Sciences, 2025)
During infectious disease outbreaks, the dissemination of information and the dynamic adjustment of intervention strategies trigger psychological and behavioral changes among individuals, which significantly influence disease transmission. Mathematical models have played a crucial role in analyzing the interplay between behavioral changes and disease spread. In this review, we revisit research studies that model behavioral changes during epidemics and classify the literature based on different modeling approaches. Specifically, we categorize these models into three main types: (1) modifying the incidence function to incorporate behavior-driven changes, including a novel approach that utilizes neural networks to describe the incidence rate; (2) introducing additional compartments to represent subpopulations with different behaviors; and (3) employing game-theoretic modeling to study the interactions between infectious disease dynamics and behavioral changes. In the game-theoretic framework, we also examine how key epidemiological metrics – such as the peak size and peak time of the first wave, as well as the final epidemic size – are affected when behavioral changes are incorporated into the classic SIR model. For each category, we introduce the classical modeling frameworks and their extensions, analyzing their advantages and limitations. Finally, we summarize the key findings and outline several promising directions for future research.