Bayesian multiscale modeling for aggregated disease mapping data

Abstract

In spatial epidemiology, a scaling effect due to an aggregation 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 propose 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 convolution models at the finer and coarser levels. All these models 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.