Private Choices in Public Health: A Framework for Economic Epidemiological Modeling of Infectious Disease

Abstract

This dissertation investigates the role of risk compensation in infectious disease dynamics by examining how individuals adjust private preventive behaviors in response to perceived mortality risk, and how these behavioral shifts shape and are shaped by epidemic trajectories. While conventional epidemiological models assume fixed or policy-driven contact rates, real-world transmission depends critically on how individuals perceive and respond to evolving risk. Public health policies, in turn, interact with these private behaviors—amplifying or dampening their effects. The first part of this dissertation provides empirical evidence of risk compensation behavior by analyzing high-frequency mobility data across U.S. counties during the COVID-19 pandemic. Using lagged local mortality as a proxy for perceived risk, the study finds that individuals significantly reduced their mobility in response to rising deaths—particularly in high-contact, discretionary domains. These responses evolved over time and were shaped by public health interventions such as shelter-in-place orders and mask mandates, which often amplified rather than displaced private behavioral changes. The second part develops a dynamic economic-epidemiological model that endogenizes contact rates through mortality-responsive behavioral feedback. Critically, the magnitude of behavioral responsiveness estimated in the empirical analysis is used to calibrate the strength of the feedback loop—bridging observed behavior with modeled transmission. This endogenous co-evolution of behavior and epidemic severity creates a self-regulating system in which private action dynamically responds to real-time risk signals, suppressing transmission as perceived threat intensifies. The model, grounded in a delayed SEIRDS framework, demonstrates how such feedback can flatten epidemic curves, delay transmission peaks, and produce multiple waves. Introducing pandemic fatigue—modeled as declining responsiveness to risk—erodes this adaptive loop, weakening the self-limiting dynamic and enabling persistent spread. Simulations highlight the importance of accounting for behavioral adaptation in forecasting and policy design. Together, these studies offer a unified framework for understanding how private choices interact with public policy and disease dynamics through the lens of risk perception and behavioral adaptation.