SGER: Towards Computational Epidemiology:Designing an Infectious Disease Outbreak Simulator

该项目的重点是计算科学的一个新的分支，即计算流行病学，这是一项与国民健康相关的跨学科、小规模、探索性、高风险的研究。具体而言，该项目将完成设计和实施传染病爆发模拟器的复杂任务。该工具不仅允许流行病学家对流行病学事件的空间进展进行假设，还允许计算机科学家研究部署通信基础设施对控制和遏制疾病爆发的影响。这标志着一个相当雄心勃勃的奋进，因为它需要解决植根于图论，随机建模，医学地理学以及高性能网络和计算的问题。开发的模拟器不仅将作为流行病学家的调查和分析计算工具，而且还将成为帮助开发医疗保健提供者之间所需通信基础设施的重要工具。它可用于调查提供流行病学家通过点对点网络（例如点对点网格计算）和/或传感器网络访问分布式信息的有效性。换句话说，使用该模拟器进行的实验研究将为如何设计通信基础设施以及如何适当分配传感器网络组件的决策提供指导。这个模拟器的设计可以被视为计算流行病学领域的早期贡献。

This project is focusing on a new branch of computational science, i.e. computational epidemiology, an interdisciplinary, small scale, exploratory, high risk research with relevance to national health. Specifically, this project will accomplish a complex task of designing and implementing a simulator of an Infectious Disease Outbreak. This tool will not only allow epidemiologist to hypothesize on the spatial progression of an epidemiological event, but will allow computer scientists to study the effects of deploying a communication infrastructure on control and containment of a disease outbreak. This marks a rather ambitious endeavor, as it requires solutions to problems that are rooted in graph theory, stochastic modeling, medical geography, and high performance networking and computing. The developed simulator will serve not only as an investigative and analytical computational tool for epidemiologists, but will also be an essential tool to help develop the required communication infrastructure among healthcare providers. It can be used to investigate the effectiveness of providing epidemiologists with access to distributed information via peer-to-peer networks (e.g. peer-to-peer grid computing) and/or access to sensor networks. In other words, experimental studies conducted with this simulator will provide guidance in the decisions how to design the communication infrastructure and how to appropriately distribute sensor network components. The design of this simulator can be viewed as an early contribution to the field of computational epidemiology.