BAYESIAN MODELS FOR SPATIO-TEMPORAL ASSESSMENT OF DISEASE

Abstract

Epidemiological data are often characterised by a spatial and/or temporal structure. To adequately account for spatial and temporal dependence in these data, there are pointbased and area-based spatial and spatio-temporal models in the literature. However, there is a lack of knowledge about the impact of modelling at different spatial scales, temporal scales and spatial structures. This is of practical interest for diseases such as cancer that can display high and low intensities over a geographical region, can be subjected to a range of socio-economic and other risk factors and can change in spatial pattern over time with demographic and other changes. Given the importance for epidemiologists to take into account the spatial correlation in a disease dataset using spatial smoothing techniques, the choice of spatial and temporal smoothness priors is an acknowledged challenge that motivates the current research. In view of the fact that the spatial and spatio-temporal models are hierarchical models in which inference and estimation are not trivial, the research is conducted using Bayesian techniques to facilitate the inference. This thesis aims to explore, assess and provide guidance on the suitability of different spatial scales, spatial smoothness priors and temporal scales in an original and comprehensive way. We focus on a rich and flexible class of Bayesian spatial and spatio-temporal models. This research endeavours to fulfil the aim by addressing the following objectives. Firstly, we discuss and evaluate a number of spatial models and their suitability for analysing various structures of spatial point patterns at the grid level. The study confirms that different models may be more appropriate for different structures of point patterns due to their varying complexity and flexibility. Spatially complicated datasets generally require a spatial prior with greater flexibility.