Disease mapping models for data with weak spatial dependence or spatial discontinuities

Baptista, Helena; Congdon, Peter; Mendes, Jorge M.; Rodrigues, Ana M.; Canhão, Helena; Dias, Sara Simões

http://hdl.handle.net/10400.8/5691

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Abstract(s)

Recent advances in the spatial epidemiology literature have extended traditional approaches by including determinant disease factors that allow for non-local smoothing and/or non-spatial smoothing. In this article, two of those approaches are compared and are further extended to areas of high interest from the public health perspective. These are a conditionally specified Gaussian random field model, using a similaritybased non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping; and a spatially adaptive conditional autoregressive prior model.