Big problems in spatio-temporal disease mapping: methods and software

TL;DR

This paper introduces a scalable, efficient method for fitting hierarchical spatio-temporal models to large areal datasets, enabling reliable disease risk estimation in high-dimensional settings.

Contribution

It proposes a novel procedure using integrated nested Laplace approximations and parallel computing to handle large-scale spatio-temporal data efficiently.

Findings

Outperforms classical models in accuracy and speed

Enables analysis of high-dimensional disease data

Provides an open-source R package for implementation

Abstract

Fitting spatio-temporal models for areal data is crucial in many fields such as cancer epidemiology. However, when data sets are very large, many issues arise. The main objective of this paper is to propose a general procedure to analyze high-dimensional spatio-temporal count data, with special emphasis on mortality/incidence relative risk estimation. We present a pragmatic and simple idea that permits to fit hierarchical spatio-temporal models when the number of small areas is very large. Model fitting is carried out using integrated nested Laplace approximations over a partition of the spatial domain. We also use parallel and distributed strategies to speed up computations in a setting where Bayesian model fitting is generally prohibitively time-consuming and even unfeasible. Using simulated and real data, we show that our method outperforms classical global models. We implement the methods and algorithms that we develop in the open-source R package bigDM where specific vignettes have been included to facilitate the use of the methodology for non-expert users. Our scalable methodology proposal provides reliable risk estimates when fitting Bayesian hierarchical spatio-temporal models for high-dimensional data.