CAREER: Advancing Disease Modeling for Decision Making in Healthcare

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This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national health and welfare by integrating individual-level and population-level models of infectious diseases to support data-driven healthcare decision making. This project aims to build a unifying theory of disease modeling to incorporate variation in human behavior that can affect epidemic trends in disease transmission, progression, and recovery. The research will provide insights into how these models can be used to improve both quality and cost-effectiveness of public health services. Partnership with clinicians and health policy makers will ensure that the models address policy concerns and advance interdisciplinary collaboration and data sharing. The educational goals of the project aim to advance STEM education among clinical collaborators and students, including those from underrepresented and minority communities.This research studies state space aggregation methods to unify individual-level simulations with compartmental models for disease modeling. The objectives are to optimize these models in single and repeated decision contexts, and to integrate these methods in cost-effectiveness analysis. This work is motivated by the need for tractable disease models that incorporate behavior and population heterogeneity, which require data-driven methods to aggregate individuals into interpretable groups while respecting the dynamics of the underlying disease process. Understanding how simulation outcomes can be projected to a lower-dimensional space and bridging the gap between individual and group-level representation in modeling will allow for these methods to better inform policy. Specifically, structural properties, equilibria, and transition dynamics of these models will be studied with the goal of using them to simplify optimization and Markov decision process frameworks on disease dynamics. Innovations in this area can then be integrated into cost-effectiveness and probabilistic sensitivity analysis to provide more efficient and thorough evaluation of the feasible policy space.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.