Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya

Model-based disease mapping remains a fundamental policy-informing tool in public health and disease surveillance. Hierarchical Bayesian models have become the state-of-the-art approach for disease mapping since they are able to capture structure in the data, as well as to characterise uncertainty. When working with areal data, e.g.~aggregates at the administrative unit level such as district or province, routinely used models rely on the adjacency structure of areal units to account for spatial correlations. The goal of disease surveillance systems is to track disease outcomes over time. This task provides challenging in situations of crises, such as political changes, leading to changes of administrative boundaries. Kenya is an example of a country where change of boundaries took place in 2010. Moreover, the adjacency-based approach ignores the continuous nature of spatial processes and cannot solve the change-of-support problem, i.e.~when administrative boundaries change or when estimates must be produced at a different administrative level. We present a novel, practical, and easy to implement solution relying on a methodology combining deep generative modelling and fully Bayesian inference: we build on the recently proposed PriorVAE method able to encode spatial priors over small areas with variational autoencoders, to map malaria prevalence in Kenya.