Statistical modelling for real-time spatial surveillance and forecasting

公共卫生结果通常与特定地点和特定时间相关，因此产生时空数据。及时分析这些数据有助于设计和迅速提供公共卫生干预措施。但是，目前大多数用于分析这类数据的统计方法都是离线操作的，而且在有关事件发生和完成对结果数据的分析之间往往有相当大的延迟。在这个项目中，我们建议开发可以实时运行的新颖统计方法，并在软件系统中实现这些方法，这些系统可以在实时积累时自动响应新获取的数据。我们将把方法论的发展嵌入到两个不同的应用程序中。第一个目标是预测和控制撒哈拉以南非洲新出现的脑膜炎流行；脑膜炎是世界这一地区的一个主要公共卫生问题，在该地区，3%至30%的人口携带感染病原体，病例死亡率可高达10%。我们的第二个应用程序是监测和报告呼叫NHS直接，按地点，时间和综合症分类；其目的是通过提供比以往更精细的空间和时间分辨率的信息，加强目前基于NHS Direct数据的监测系统。

Public health outcomes are typically associated with both a specific place and a specific time, and therefore generate spatio-temporal data. Timely analysis of these data can assist in both the design and the prompt delivery of public health interventions. However, most currently available statistical methods for the analysis of such data operate off-line, and often with considerable delay between the occurrence of the events in question and completion of the analysis of the resulting data. In this project, we propose to develop novel statistical methods that can be run in real-time, and to implement these in software systems that respond automatically to newly acquired data as they accrue in real-time.We will embed the methodological developments within two contrasting applications. The first is to the forecasting and control of emerging meningitis epidemics in sub-Saharan Africa; Meningitis is a major public health problem in this part of the world, where between 3% and 30% of the population carries the infective agent, and the mortality rate amongst cases can be as high as 10%. Our second application is to the monitoring and reporting of calls to NHS Direct, classified by place, time and syndrome; the aim is to strengthen current surveillance systems based on NHS Direct data, by providing information at finer spatial and temporal resolutions than hitherto.