Statistical modelling for real-time spatial surveillance and forecasting

实时空间监测和预测的统计建模

• 批准号: G0902153/1
• 负责人: Peter Diggle
• 金额: $ 51.26万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G0902153%2F1
• 关键词: Statistical; modelling; real; time; spatial

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.

公共卫生结果通常与特定地点和特定时间相关联，因此产生时空数据。及时分析这些数据有助于设计和及时提供公共卫生干预措施。然而，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，而且在所涉事件发生和完成对所产生数据的分析之间往往有相当大的延迟。在这个项目中，我们建议开发新的统计方法，可以在实时运行，并在软件系统中实现这些自动响应新获得的数据，因为他们在实时accumulation.We将嵌入在两个对比的应用程序的方法的发展。第一个目标是预测和控制撒哈拉以南非洲地区新出现的脑膜炎流行病;脑膜炎是世界这一地区的主要公共卫生问题，其中3%至30%的人口携带感染因子，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，时间和综合征分类;目的是加强目前的监测系统的基础上NHS直接数据，提供信息，在更精细的空间和时间分辨率比迄今为止。