During epidemic outbreaks, populations adapt their behavior in response to disease burden, fundamentally altering transmission dynamics. Despite this, most compartmental models assume constant contact rates throughout outbreaks. To quantify biases from this assumption, we fitted a baseline SEIRD model with constant transmission and three behavioral variants—incorporating mortality-driven transmission reduction via exponential, rational, and mixed functional forms—to COVID-19 mortality data from 30 US locations during the first pandemic wave (March–July 2020). All three behavioral models achieved a lower median normalized sum of squared error in at least 28 of 30 locations, and Bayesian model selection favored them in at least 28 of 30 locations. More importantly, we identified systematic biases when behavioral responses are ignored: the baseline model consistently underestimated the basic reproduction number (ℛ0) while paradoxically overestimating final epidemic size. Median ℛ0 estimates from behavioral models exceeded baseline estimates across all 30 locations, yet baseline models predicted larger cumulative infection burdens. Controlled synthetic experiments—where mortality trajectories were generated from behavioral models with known parameters—confirmed these biases result from model mis-specification rather than data quality or stochastic variation. We prove analytically that for any fixed ℛ0, the baseline model overestimates cumulative infections compared to behavioral models where mortality reduces transmission, regardless of functional form. This dual bias poses serious risks for pandemic response: standard models may simultaneously underestimate pathogen contagiousness (delaying critical early action) while overestimating infection burden (causing excessive late-phase resource allocation). Our findings across 30 geographically diverse locations demonstrate that incorporating behavioral change substantially improves both model fit and estimation of epidemiological parameters essential for public health policy.
