Improving policy design and epidemic response using integrated models of economic choice and disease dynamics with behavioral feedback

Human behavior plays a crucial role in infectious disease transmission, yet traditional models often overlook or oversimplify this factor, limiting predictions of disease spread and the associated socioeconomic impacts. Here we introduce a feedback-informed epidemiological model that integrates human behavior with disease dynamics in a credible, tractable, and extendable manner. From economics, we incorporate a dynamic decision-making model where individuals assess the trade-off between disease risks and economic consequences, and then link this to a risk-stratified compartmental model of disease spread taken from epidemiology. In the unified framework, heterogeneous individuals make choices based on current and future payoffs, influencing their risk of infection and shaping population-level disease dynamics. As an example, we model disease-decision feedback during the early months of the COVID-19 pandemic, when the decision to participate in paid, in-person work was a major determinant of disease risk. Comparing the impacts of stylized policy options representing mandatory, incentivized/compensated, and voluntary work abstention, we find that accounting for disease-behavior feedback has a significant impact on the relative health and economic impacts of policies. Including two crucial dimensions of heterogeneity—health and economic vulnerability—the results highlight how inequities between risk groups can be exacerbated or alleviated by disease control measures. Importantly, we show that a policy of more stringent workplace testing can potentially slow virus spread and, surprisingly, increase labor supply since individuals otherwise inclined to remain at home to avoid infection perceive a safer workplace. In short, our framework permits the exploration of avenues whereby health and wealth need not always be at odds. This flexible and extendable modeling framework offers a powerful tool for understanding the interplay between human behavior and disease spread.Author summary: Models help researchers and policymakers predict how infections spread and compare control strategies. However, current models neglect how behavioral choices (like social distancing or vaccination) influences and reacts to disease spread. We present a new model combining ideas from epidemiology and economics to describe feedback between individual decisions, population health, and economic outcomes. Simulated individuals evaluate their future infection risk and weigh the costs/benefits of possible actions. Different health or economic vulnerabilities lead to distinct trade-offs and behaviors. We model the early stage of COVID-19 when people had to choose between going to work and risking infection or staying home and losing income. More generally, our model provides a flexible tool for policymakers to compare interventions to reduce disease, limit costs, and prevent disparities.