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Large-scale Social Simulations for Public Health: Computational Challenges and Opportunities


John J. Grefenstette
Director, Public Health Dynamics Laboratory
and Professor of Biostatistics
Graduate School of Public Health, University of Pittsburgh

Thursday, 16 February 2012
1:30 PM – Reddy Conference Room, Gates & Hillman Centers 4405

Abstract

The accelerating growth in data availability and corresponding advances in high performance computing present new opportunities for in silico analysis of complex public health questions using computational modeling and simulation. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an open source, modeling system developed by the University of Pittsburgh Public Health Dynamics Laboratory (PHDL) in collaboration with the Pittsburgh Supercomputing Center and the School of Computer Science at Carnegie Mellon University. FRED supports research on the dynamics of infectious disease epidemics, and the interacting effects of mitigation strategies, viral evolution, and personal health behavior. The system uses agent-based modeling based on census-based synthetic populations that capture the demographic and geographic distributions of the population, as well as detailed household, school, and workplace social networks. In this talk, we will describe how we use simulations to help evaluate health policy questions concerning areas such as vaccination, anti-viral drugs, and school closure. We will also discuss challenges including modeling dynamic social structures and incorporating more sophisticated models of human health behavior into our models.

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John J. Grefenstette, Ph.D., is the Director of the Public Health Dynamics Laboratory and Professor of Biostatistics in the Graduate School of Public Health at the University of Pittsburgh. He previously served as Professor and Chair of the Department of Bioinformatics and Computational Biology and Assistant Dean for the School of Computational Sciences at George Mason University, and as Head of the Machine Learning Section at the Navy Center for Applied Research in Artificial Intelligence at the U.S. Naval Research Laboratory. Dr. Grefenstette's research activities span many areas on the boundary between computation and biology, including modeling and simulation of infectious diseases, public health databases, data mining, evolutionary algorithms, machine learning, computational models of biological networks, and high-performance computing applications to public health. His current projects include the University of Pittsburgh's National Center of Excellence for Models of Infectious Diseases Agent Study (MIDAS), funded by NIH/NIGMS, and the Vaccine Modeling Initiative, funded by the Bill and Melinda Gates Foundation. Dr. Grefenstette received his BS in Mathematics and Philosophy from Carnegie Mellon University and his PhD in Computer Science from the University of Pittsburgh. In 2010, Dr. Grefenstette was honored with the Evolutionary Computation Pioneer Award from the IEEE Computational Intelligence Society.

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