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

受利用常规收集的调查数据了解疾病风险的空间差异这一问题的推动，该项目旨在开发跨越长时间段的空间聚集数据的模型和统计推断方法。在研究疾病风险的小区域变异时，需要高分辨率的疾病风险预测，无法获得确切的病例位置这一事实变得重要。此外，通常提供数据的人口普查地区和邮政地区的规模差别很大，在城市地区信息量很大，在人口较少的地区受到严格审查，这些地区在地图上占据主导地位。对小区域或罕见结果的研究需要很长的研究时间才能积累足够的病例。这带来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。

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.