Bayesian multiscale modeling for aggregated disease mapping data

In spatial epidemiology, a scaling effect due to an aggre-gation of data from a finer to a coarser level is a common phenomenon. This article focuses on addressing this issue using a hierarchical Bayesian modeling framework. We pro-pose three different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third one assumes two separate convo-lution models at the finer and coarser levels. All these mod-els were compared based on deviance information criterion (DIC), Watanabe-Akaike or widely applicable information criterion (WAIC) and predictive accuracy applied on real and simulated data. The results indicate that the models with a shared random effect outperform the other models. Categories and Subject Descriptors G.3 [Probability and Statistics]: Statistical computing, Spatial data analysis