Speaker
Description
Behavioral responses during epidemics alter transmission, yet most epidemiological models assume constant contact rates. We assessed the resulting bias by fitting a baseline SEIR model with fixed transmission and three behavioral variants, in which transmission declines with increasing mortality, to COVID-19 mortality data from 30 U.S. states during the first wave (March-July 2020).
Behavioral models fit the data better in 28 of 30 states, with Bayes factors giving substantial to decisive support in those same states. Ignoring behavioral feedback produced consistent inferential errors: baseline models systematically underestimated the basic reproduction number (R0) while simultaneously overestimating the final epidemic size. Posterior R0 estimates were higher under behavioral models across all states, yet baseline models predicted substantially larger cumulative infection burdens.
Synthetic-data experiments showed that these discrepancies are caused by model misspecification rather than noise or data limitations. Analytically, we show that for fixed R0, models without behavioral feedback overestimate epidemic size whenever mortality reduces transmission. Explicit behavioral modeling is therefore essential for reliable epidemic inference and forecasting.
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