The paradox of neglecting changes in behavior: How standard epidemic models misestimate both transmissibility and final epidemic size

Abstract

Abstract 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 (ℛ 0 ) while paradoxically overestimating the final epidemic size. Median ℛ 0 estimates from the behavioral models exceeded the 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 misspecification rather than data quality or stochastic variation. We prove analytically that for any fixed ℛ 0 , 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. Author Summary When diseases spread, people change their behavior—avoiding crowds, wearing masks, washing hands more frequently. Yet most mathematical models used to predict epidemics assume people behave the same way throughout an outbreak. We asked: What happens when models ignore these behavioral changes? Using COVID-19 data from 30 US locations, we compared a traditional model (assuming constant behavior) against models where people reduce contact as deaths increase. We discovered a troubling paradox: models ignoring behavior consistently suggest diseases are less contagious than they really are, yet simultaneously predict that more people will get infected. This creates a dangerous mismatch for decision-makers: underestimating how easily a disease spreads may delay urgent early actions like school closures or travel restrictions, while overestimating total infections wastes resources preparing for scenarios that behavioral adaptation prevents. We confirmed this paradox through computer simulations where we knew the true answer, and proved analytically that behavioral adaptation reduces final epidemic size even when the basic reproduction number is held constant. Our work shows that incorporating human behavioral responses is not just a modeling refinement—it is essential for accurate epidemic predictions that inform life-or-death policy decisions.

The authors' abstract, as published at the source. PLoS Computational Biology, 2026 · DOI ↗