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

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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.