RAPID: Stochastic Ebola Modeling on Dynamic Contact Networks

随着世界面临埃博拉疫情的大规模爆发，目前有几种疫苗正在进行临床试验，有合理的机会在2015年初在实地使用。由于最初的疫苗数量有限，了解埃博拉疫情动态对于通过有针对性的疫苗接种、监测和检疫相结合来最大限度地提高公共卫生干预的有效性至关重要。本项目关注的是开发一个现实的、同时在数学上易于处理的、统计上可预测的当前世界范围埃博拉疫情动态模型。 将人口分为三组（易感人群、感染人群和清除人群--所谓的SIR模型）的建模方法及其各种推广方法历来被用作决定流行病是增长还是消散的重要工具。 研究人员在具有给定度分布的图上扩展了SIR随机流行病的传统模型，以考虑埃博拉特有的特征。 这些措施包括，除其他外，纳入一类高感染风险的个人（例如，新的数学模型描述了埃博拉病毒通过农村和城市地区以及各国和各大洲的人类接触网络传播的方式，将根据来自世界卫生组织的实际实地数据提供信息。 包括非洲和美国在内的世界各地。预计该模型将使公共卫生和政府官员能够快速分析一系列不同的干预方案，以加快当前流行病的消散，并选择最有效的方法来预防或至少最大限度地减少未来的疫情爆发。

As the world faces a large outbreak of Ebola epidemic, several vaccines are currently in clinical trials with a reasonable chance of being available in the field in early 2015. Since the initial amount of vaccine will be limited, the understanding of Ebola epidemic dynamics is essential for maximizing the effectiveness of public health intervention through a combination of targeted vaccination, monitoring and quarantine.This project is concerned with developing a realistic but at the same time mathematically tractable and statistically predictive dynamic model of the current world-wide Ebola epidemics. The modeling approach that divides the population into three groups (susceptibles, infected, and removed--the so-called SIR model) and its various generalizations has been used historically as an important tool in deciding whether epidemics grow or dissipate. The investigators expand the traditional model of an SIR stochastic epidemic on a graph with a given degree distribution, in order to account for the Ebola-specific features. These include, among others, incorporating a class of individuals at high risk of infection (e.g., health workers), and incorporating a dynamic network structure that reflects how contacts with different segments of the population change over the course of infection within host.The new mathematical model describing the way in which Ebola spreads through a network of human contacts, both in rural and urban areas as well as across countries and continents, will be informed by the actual field data from various parts of the world including Africa and the United States. It is expected that the model will allow public health and government officials to quickly analyze a host of different intervention scenarios in order to speed up the current epidemic's dissipation and to select the most effective way of preventing, or at least minimizing, the future outbreaks.