disaggregation: An R Package for Bayesian Spatial Disaggregation Modelling

TL;DR

This paper introduces an R package designed to facilitate Bayesian spatial disaggregation modeling, enabling detailed epidemiological analysis from aggregated regional data using covariates.

Contribution

The paper presents the disaggregation R package, offering a streamlined tool for Bayesian spatial disaggregation modeling in epidemiology.

Findings

Enables fine-scale predictions from aggregated data.

Integrates covariates for heterogeneity modeling.

Simplifies disaggregation modeling process.

Abstract

Disaggregation modelling, or downscaling, has become an important discipline in epidemiology. Surveillance data, aggregated over large regions, is becoming more common, leading to an increasing demand for modelling frameworks that can deal with this data to understand spatial patterns. Disaggregation regression models use response data aggregated over large heterogenous regions to make predictions at fine-scale over the region by using fine-scale covariates to inform the heterogeneity. This paper presents the R package disaggregation, which provides functionality to streamline the process of running a disaggregation model for fine-scale predictions.