Inference on Spatio-Temporal Log-Gaussian Cox Processes for Spatially Aggregated Disease Incidence Data

空间聚合疾病发病率数据的时空对数高斯 Cox 过程的推断

• 批准号: 342306-2012
• 负责人: Brown, Patrick
• 金额: $ 0.87万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=549621
• 关键词: Inference; Spatio; Temporal; Log; Gaussian

Motivated by the problem of understanding spatial variations in disease risk using routinely collected survey data, this project aims to develop models and statistical inference methods for spatially aggregated data spanning long time periods. When studying small-area variations in disease risk, where prediction of disease risk at a high resolution is required, the fact that exact case locations are unavailable becomes important. Further, the census regions and postal regions on which data are typically made available vary greatly in size being highly informative in urban areas and heavily censored in the less populated ares which dominate maps. Studies on small regions or rare outcomes require long study periods to accumulate sufficient cases. This introduces the problem of risk changing over time as well as changes to census and postal regions.

利用常规收集的调查数据了解疾病风险的空间变化问题，本项目旨在为长时间的空间聚合数据开发模型和统计推断方法。当研究疾病风险的小区域变化时，需要以高分辨率预测疾病风险，无法获得确切的病例位置这一事实变得很重要。此外，通常提供数据的人口普查区域和邮政区域在大小上差别很大，在城市地区资料丰富，而在地图上占主导地位的人口较少的地区则受到严格审查。小区域或罕见结果的研究需要较长的研究周期才能积累足够的病例。这就引入了风险随时间变化的问题，以及人口普查和邮政地区的变化。