disaggregation: An R Package for Bayesian Spatial Disaggregation Modeling

• DOI: 10.48550/arxiv.2001.04847
• Publication Date: 2023-01-01
• Authors: Nandi, Anita K.; Lucas, Tim C. D.; Arambepola, Rohan; Gething, Peter; Weiss, Daniel J.
• Keywords: FOS: Computer and information sciences; TMB; Statistics; Statistics - Computation; 01 natural sciences; HA1-4737; Methodology (stat.ME); 0101 mathematics; disaggregation modelling; Statistics - Methodology; Computation (stat.CO); Bayesian spatial modelling

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.<br/>16 pages, 5 figures, submitted to Journal of Statistical Software<br/>