Collaborative Research: Modeling Interaction Between Individual Behavior, Social Networks And Public Policy To Support Public Health Epidemiology.

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Abstract The project aims to bridge the gap between social sciences, public health and computer science in order to address an important problem in epidemiology. The research focus is on the development of high fidelity computational models for understanding the aggregate effects for interactions among individual behavior, social networks and public health policies. The results will support local and federal officials in their planning and response to the spread of infectious diseases, e.g. pandemic avian influenza. The computational agent-based models will yield practical methods to support officials in decision making both before an outbreak, by identifying critical normative individuals in urban societies, and during an outbreak, by identifying the potential cascading effects of individual and group behavior within social networks. The results will be integrated into a functioning high-fidelity agent based simulation and modeling capability. The project has the following components: (1). Computational agent-based models of individual behavior and its interaction with social networks and public policy; (2) Theoretical investigation using game theory and discrete dynamical systems of the dynamic co-evolution of social networks, individual behavior and public policies; and (3) Illustrative realistic case studies that demonstrate the results of our research to aid in planning for and responding to large scale infectious disease outbreaks.