  Short-term disease forecasting at specific discrete spatial resolutions has
become a high-impact decision-support tool in health planning. However, when
the number of areas is very large obtaining predictions can be computationally
intensive or even unfeasible using standard spatio-temporal models. The purpose
of this paper is to provide a method for short-term predictions in
high-dimensional areal data based on a newly proposed ``divide-and-conquer"
approach. We assess the predictive performance of this method and other
classical spatio-temporal models in a validation study that uses cancer
mortality data for the 7907 municipalities of continental Spain. The new
proposal outperforms traditional models in terms of mean absolute error, root
mean square error and interval score when forecasting cancer mortality one, two
and three years ahead. Models are implemented in a fully Bayesian framework
using the well-known integrated nested Laplace (INLA) estimation technique.
