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PRECISION MEDICINE AND THE MANAGEMENT OF INFECTIOUS DISEASES

Abstract

Infectious diseases place an enormous toll on public health, societies, and economies across the world. Effective control of an infectious disease is made challenging by complex disease dynamics, limited resources, and the need to continually adapt interventions to the evolving status of an epidemic. Driven by widespread recognition of the potential of big data, recent technological advances have made it possible to collect, curate, and access heterogeneous data on the evolution of an infectious disease in real-time. In light of recent and anticipated advances in big data, this project envisions that future management of infectious diseases will rely on knowledge-transfer systems that map real-time, heterogeneous data streams to recommendations for policy-makers managing an infectious disease. These recommendations might identify subgroups in the population that should be given highest priority for interventions or other resource allocations. However, science is years away from creating such a system as its implementation will require significant innovations in disease modeling, data-driven decision making, computing, and optimization. This award supports initiation of a collaborative research project that takes critical first steps toward these innovations by creating a blueprint for using big data and precision medicine to inform management of an infectious disease. The proposed research will develop a novel class of dynamical systems models that identifies subgroups in the population with homogeneous disease dynamics. Parameters indexing these models are estimated using maximum likelihood; then, by computing draws from the sampling distribution of these parameters, we apply Thompson sampling and model-based policy-search algorithms from precision medicine to identify optimal resource allocations to manage the spread of an epidemic. The proposed research bridges dynamical systems models with subgroup identification and data-driven resource allocation. This will create new knowledge and new lines of research in applied mathematics, statistics, and computer science. This award is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative in partnership with the National Science Foundation Division of Mathematical Sciences.

People

Jin Wang

Professor and UNUM Chair of Excellence
University of Tennessee Chattanooga

Funding Source

Project Period

2015-2017