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RAISE: IHBEM: Modeling Dynamic Disease-Behavior Feedbacks for Improved Epidemic Prediction and Response

Abstract

Epidemiological models inform policymakers about how infectious diseases like COVID-19 may spread through the population. These predictions guide the allocation of health resources and interventions to reduce disease spread or mitigate its burdens. While these models incorporate information about how an infection is transmitted, the disease course in infected individuals, and the effects of interventions like vaccines, they rarely capture how individuals make behavior decisions or how these choices respond to an epidemic. As individuals face different economic and health circumstances, the population will not uniformly respond to the epidemic or policy interventions. This omission materially affects the accuracy of these models and by extension, the effectiveness of policies deployed to combat the disease and its impacts. To address this limitation, this project brings together expertise in epidemiology, mathematical biology, systems engineering, economics, and decision science at Johns Hopkins University to develop a new integrated modeling framework that combines traditional epidemiological models of disease spread with economic models of individual decision-making. The three most significant benefits of this new approach are: 1) Improving future epidemic forecasts for existing and new emerging infections; 2) Allowing for a cost-benefit analysis of disease mitigation policies that reflects changes in human behavior and economic outputs; 3) Helping predict the disparate impact of an epidemic and mitigation policies across socioeconomic groups facing different health-wealth tradeoffs. The broader impacts of the project include dissemination of research results to broad audience and training of public health practitioners. The tools to understand the complex dynamic interactions between human behavior and pathogens during disease emergence, dissemination, and control are lacking. Current epidemiological models generally do not endogenize individual behaviors, while agent-based models from economics that have this feature miss critical aspects of disease transmission and progression. The goals of this study are to: 1) More accurately predict disease spread and health outcomes during an outbreak, epidemic, or pandemic; 2) Enable multi-objective policy design by simultaneously quantifying both the disease burden and economic costs of proposed policies, allowing for the evaluation of both economic and health policies; and 3) Evaluate heterogeneity and equity by quantifying the distributional impacts of disease burden and economic cost across socio-demographic and risk groups. To address these goals, this project brings together a multi-disciplinary team at Johns Hopkins—with expertise in epidemiology, mathematical biology, systems engineering, economics, and decision science—to develop a novel integrated mathematical framework that combines mechanistic models of infectious disease dynamics with economic models of human behavior. This framework is designed to capture behavioral responses to both the epidemic state and policies in place, and the effect of individual-level behavioral responses on the trajectory of the disease within a population. This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Funding Source

Project Period

2022-2025

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