Spline smoothing in Bayesian disease mapping
نویسندگان
چکیده
In this paper, a class of Bayesian hierarchical disease mapping models with spline smoothing are motivated and developed for sequential disease mapping and for surveillance of disease risk trends and clustering. The methodological development aims to provide reliable information about the patterns (both over space and time) of disease risk and to quantify uncertainty. Bayesian disease mapping models with B-splines, smoothing splines and P-splines are developed respectively and a comparison of the three smoothing methods in the context of risks ensemble prediction is presented. The methods are illustrated through a Bayesian analysis of iatrogenic injuries to hospital in-patients in British Columbia, Canada.
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