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

该项目的动机是利用常规收集的调查数据了解疾病风险的空间变化，目的是为跨越长时间的空间汇总数据开发模型和统计推断方法。 在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，无法获得确切病例位置的事实变得很重要。 此外，通常提供数据的人口普查区和邮政区在大小上差别很大，在城市地区信息量很大，而在人口较少的战神（占地图的主导地位）则受到严重审查。 对小区域或罕见结果的研究需要长时间的研究来积累足够的病例。 这就带来了风险随时间变化以及人口普查和邮政区域变化的问题。

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.