  Model-based disease mapping remains a fundamental policy-informing tool in
the fields of public health and disease surveillance. Hierarchical Bayesian
models have emerged as the state-of-the-art approach for disease mapping since
they are able to both capture structure in the data and robustly characterise
uncertainty. When working with areal data, e.g.~aggregates at the
administrative unit level such as district or province, current models rely on
the adjacency structure of areal units to account for spatial correlations and
perform shrinkage. The goal of disease surveillance systems is to track disease
outcomes over time. This task is especially challenging in crisis situations
which often lead to redrawn administrative boundaries, meaning that data
collected before and after the crisis are no longer directly comparable.
Moreover, the adjacency-based approach ignores the continuous nature of spatial
processes and cannot solve the change-of-support problem, i.e.~when estimates
are required to be produced at different administrative levels or levels of
aggregation. We present a novel, practical, and easy to implement solution to
solve these problems 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 by encoding aggregates over administrative units. We map malaria
prevalence in Kenya, a country in which administrative boundaries changed in
2010.
