Bivariate spatial clustering in differential time trends of related tropical diseases: Application to diarrhea and intestinal parasite infections

There has been a rapid development of space–time multivariate disease mapping methods that focus on space–time variation of risks. Examining the posterior estimates of the space–time random effects can provide compelling epidemiological information that is necessary for public health monitoring. In this study, we propose and evaluate the posterior estimates of the random effects to examine spatial and temporal trends of two tropical diseases. Our model is a multivariate Bayesian space–time model with common spatial and temporal trends, and a space–time interaction term that allows different time trends for different areas. When applied to diarrhea and intestinal parasites data from Ghana, the model that consolidates all random effects as a multivariate conditional autoregressive prior was the best fit. The implementation is based on the notion that diarrheal and intestinal parasite infections share common risk factors Our novel contribution concerns the posterior joint evaluations of the spatial and temporal random effects into a 5 × 5 cross-tabulation table. The method presented is useful for developing and implementing joint epidemiological control strategies, especially in countries where resources are scarce.