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

该项目旨在弥合社会科学、公共卫生和计算机科学之间的差距，以解决流行病学中的一个重要问题。研究重点是发展高保真计算模型，以理解个人行为、社会网络和公共卫生政策之间相互作用的总体效应。研究结果将支持地方和联邦官员规划和应对传染病的传播，例如大流行性禽流感。基于计算主体的模型将产生实用的方法，在疫情爆发前通过识别城市社会中的关键规范个体，以及在疫情爆发期间通过识别社会网络中个人和群体行为的潜在级联效应，支持官员做出决策。结果将集成到一个功能高保真的基于代理的仿真和建模能力。本项目由以下几个部分组成：(1)。基于计算主体的个体行为模型及其与社会网络和公共政策的相互作用；(2)运用博弈论和离散动力系统对社会网络、个体行为和公共政策的动态协同进化进行理论研究；(3)说明性现实案例研究，展示我们的研究成果，以帮助规划和应对大规模传染病爆发。

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.