Essays on Modeling Human Behavior During Epidemics: Simulation, Statistical, and Optimization Approaches

Abstract

Human behavior is at the core of epidemics. Public risk perception shapes compliance with non- pharmaceutical interventions, mobility and contact patterns, and vaccine uptake; in turn, these behaviors alter transmission dynamics and future perceptions. A central challenge in integrating behavior into epidemiological analysis is that perception and response are not instantaneous. Information diffuses through societies with delays, and behavioral adjustment often occurs gradually and asymmetrically responding differently when risk is rising than when it is falling. Ignoring these delay structures can bias empirical inference about behavioral responsiveness and can misstate the effects of policies evaluated using models that treat behavior as exogenous or contemporaneous. This dissertation advances the modeling and estimation of behavioral feedback in epidemics by focusing on how delayed risk perception links epidemic indicators to behavioral change and policy outcomes. The first essay develops and validates a delay-aware empirical framework for estimating how mobility responds to epidemic risk. Using synthetic experiments, it shows that assuming immediate response (or relying on ad hoc fixed lags) can yield biased estimates of both the magnitude and timing of behavioral response. The essay introduces a structured approach to representing perception delays using distributed-lag formulations motivated by information diffusion and provides practical methods for estimating delay parameters alongside behavioral sensitivity. The second essay extends the framework by allowing delay structures to be asymmetric across phases of the epidemic, recognizing that behavioral responses to increasing risk may differ from responses to declining risk. Through additional synthetic tests and application to U.S. state-level COVID-19 mobility data, the essay demonstrates that the assumed delay structure materially affects inference about responsiveness and can change conclusions about how quickly behavior adjusts to worsening versus improving conditions. The third essay connects behavioral estimation to policy design by examining optimal vaccination strategies under endogenous, delayed behavioral feedback. It compares a conventional SEIRV framework with constant contact rates to a behavioral SEIRbV framework in which perceived risk reduces contacts with a perception delay. In both a homogeneous setting and an age-stratified allocation setting, the analysis shows that accounting for behavioral feedback can shift suppression thresholds and the relative performance of vaccination strategies, highlighting the marginal importance of operational levers such as earlier starts and faster rollout alongside prioritization rules. Taken together, the three essays show that delays in risk perception are a first-order feature of epidemic systems. By providing methods to estimate delay-aware behavioral responses and demonstrating how behavioral feedback reshapes vaccination policy evaluation, this dissertation contributes tools and evidence to improve inference, forecasting, and the design of effective interventions in epidemic settings.