Non-stationary Bayesian Spatial Model for Disease Mapping based on Sub-regions

TL;DR

This paper introduces a non-stationary Bayesian spatial model for disease mapping that captures complex spatial dependence patterns in irregular lattice data, demonstrated through dengue risk analysis in Brazil.

Contribution

It extends the Besag model to a non-stationary framework with multiple precision parameters and a joint penalized complexity prior, enhancing interpretability and flexibility.

Findings

Improved modeling of dengue risk in Brazil.

Ability to capture non-stationary spatial dependence.

Provides an R package 'fbesag' for practical application.

Abstract

This paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is to improve the model's ability to capture complex spatial dependence patterns and increase interpretability. The proposed model uses multiple precision parameters, accounting for different intensities of spatial dependence in different sub-regions. We derive a joint penalized complexity prior for the flexible local precision parameters to prevent overfitting and ensure contraction to the stationary model at a user-defined rate. The proposed methodology can be used as a basis for the development of various other non-stationary effects over other domains such as time. An accompanying R package 'fbesag' equips the reader with the necessary tools for immediate use and application. We illustrate the novelty of the proposal by modeling the risk of dengue in Brazil, where the stationary spatial assumption fails and interesting risk profiles are estimated when accounting for spatial non-stationary.