Not everyone panics alike: When heterogeneous behavioral responses change epidemic outcomes.

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 when is treating societal behavior as a single average feedback loop a safe simplification? We develop a two-group compartmental behavioral epidemic model in which groups differ in combinations of infection fatality ratio, susceptibility, and contact rates, which leads to distinct behavioral responses to the same epidemic signals. We show increased variation in mortality risk (through different infection fatality ratio) alters dynamics: homogeneous assumptions can lead to underestimated prevalence and overestimated fatality. In contrast, heterogeneity in susceptibility alone reduces cumulative cases and deaths compared to homogeneous assumptions. Furthermore, differences in mixing patterns specifically amplify the effects of mortality risk heterogeneity. Overall, a counterintuitive result emerges: a lower-risk group which leads to a weaker response reaches herd immunity early, indirectly shielding a more cautious (i.e., responsive), higher-risk group. We further extend the model to nine 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.