Modeling dynamic disease-behavior feedbacks for improved epidemic prediction and response

Abstract

Human behavior significantly influences infectious disease transmission, yet traditional models often overlook this factor, limiting predictions of disease and the associated socioeconomic impacts. We introduce a feedback-informed epidemiological model that integrates economic decision-making with infectious disease dynamics. Individuals weigh costs and benefits, then choose behaviors that influence their risk of infection and disease progression, thereby shaping population-level dynamics. Applying this model to a scenario based on the early COVID-19 pandemic, we examine decisions to abstain from work to mitigate infection risk. Our findings reveal that feedback between disease and behavior notably affect infection rates and overall welfare, especially when accounting for individual economic and health vulnerabilities, which are often in tension. We evaluate counterfactual policies, including labor restrictions and cash transfers, illustrating how our framework can simultaneously address epidemiological, economic, and equity-related questions. This flexible and extendable modeling framework offers a powerful tool for assessing infectious disease interventions.