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RAISE: IHBEM: Equilibrium, Network Formation, and Infectious-Disease Spread: Bridging the Divide between Mathematical Biology and Economics

Abstract

This project will bridge the gap between mathematical and statistical modeling of epidemic processes and economic models of human behavior. The COVID-19 pandemic has demonstrated the need for a new suite of mathematical models and corresponding inference tools to better understand the interdependence between the spread of infectious disease and changing human behaviors. Exciting progress has been made separately in mathematical biology and in economics. This project takes a convergent approach, incorporating how humans respond and make decisions as an epidemic unfolds, using key ideas and methodologies from both disciplines. This is critical to enable reliable forecasting and effective, real-time policy. This project is funded jointly 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). In this project, an economic (game-theoretic) model of individual decision-making is integrated into a continuous-time Markov chain framework for disease and contact dynamics. Under this approach, epidemic and contact network rates are affected by individually optimal agent behavior, which in turn depend on agents' (unobservable) social-economic costs. Likelihood-based inference methods to accommodate time-inhomogeneous rate parameters are derived, considerably extending the flexibility and realism of existing models, alongside rigorous algorithms for parameter estimation and uncertainty quantification. Bayesian data augmentation is used to account probabilistically for unobservable quantities as well as missing data via latent variables. Model selection using information-theoretic criteria simultaneously quantifies the improvement in fit reaped from behavioral models and guards against overfitting. Based on learned model parameters, proposed models are tested, possible outbreak scenarios are evaluated, and the impacts of alternative policies are compared via simulation studies and analyses of limiting behaviors. 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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