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。研究的重点是开发高保真计算模型，以了解个人行为、社会网络和公共卫生政策之间相互作用的综合影响。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。基于计算代理的模型将产生实用的方法，通过在疫情爆发前识别城市社会中的关键规范性个人，以及在疫情爆发期间通过识别社交网络中个人和群体行为的潜在连锁影响，为官员的决策提供支持。结果将被集成到基于高保真代理的正常运行的模拟和建模能力中。该项目包括以下组成部分：(1)。基于计算主体的个人行为模型及其与社会网络和公共政策的互动；(2)使用博弈论和离散动力系统的社会网络、个人行为和公共政策的动态共同进化的理论研究；以及(3)说明性的现实案例研究，展示我们的研究结果，以帮助规划和应对大规模传染病爆发。

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.