A Bayesian Multivariate Model With Temporal Dependence on Random Partition of Areal Data for Mosquito-Borne Diseases

More than half of the world's population is exposed to mosquito-borne diseases, leading to millions of cases and hundreds of thousands of deaths every year. Analyzing this type of data is complex and poses several interesting challenges, mainly due to the usually vast geographic area involved, the peculiar temporal behavior, and the potential correlation between infections. Motivation for this work stems from the analysis of tropical disease data, namely, the number of cases of dengue and chikungunya, for the 145 microregions in Southeast Brazil from 2018 to 2022. As a contribution to the literature on multivariate disease data, we develop a flexible Bayesian multivariate spatio-temporal model where temporal dependence is defined for areal clusters. The model features a prior distribution for the random partition of areal data that incorporates neighboring information. It also incorporates an autoregressive structure and terms related to seasonal patterns into temporal components that are disease- and cluster-specific. Furthermore, it considers a multivariate directed acyclic graph autoregressive structure to accommodate spatial and inter-disease dependence. We explore the properties of the model through simulation studies and show results that prove our proposal compares well to competing alternatives. Finally, we apply the model to the motivating dataset with a twofold goal: finding clusters of areas with similar temporal trends for some of the diseases and exploring the existence of correlation between two diseases transmitted by the same mosquito.