Speaker
Description
Compartmental epidemic models increasingly capture demographic and contact heterogeneities, yet behavioral responses are typically treated as uniform: as cases or deaths rise, an average person perceives greater risk and increases compliance with non-pharmaceutical interventions (e.g., masking), reducing transmission. But is treating societal behavior as a single average feedback loop a safe simplification? We first develop a two-group compartmental behavioral epidemic model in which groups differ in infection fatality ratio, susceptibility, and contact rates, generating distinct behavioral responses to the same epidemic signals. We show increased variation in mortality risk alters dynamics: homogeneous assumptions lead to underestimated prevalence and overestimated fatality. Heterogeneity in susceptibility alone reduces cumulative cases and deaths, and differences in mixing patterns amplify these effects. A counterintuitive result emerges: a lower-risk group responding weakly reaches herd immunity early, indirectly shielding a more cautious, higher-risk group. We further extend the model to eight age groups reflecting COVID-19 risk variation, examining how behavioral heterogeneity shapes outcomes at a finer scale. Together, these findings clarify when explicitly representing heterogeneous behavioral responses is essential for reliable epidemic modeling.
