disaggregation: An R Package for Bayesian Spatial Disaggregation\n Modelling

2020/01/09 by A. Nandi, Tim Lucas, Nandi, Anita K. +7 · 2 citations
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Computation (stat.CO) #FOS: Computer and information sciences #Health disparities and outcomes #Methodology (stat.ME) #Spatial and Panel Data Analysis #Statistical Methods and Bayesian Inference #Urban, Neighborhood, and Segregation Studies

paper · pdf · doi:10.48550/arxiv.2001.04847

openalex publication_date 2020/01/09 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28

Abstract

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

Citations

• TMB: Automatic Differentiation and Laplace Approximation
• Algorithm 887

Cited by

• A Bayesian Geoadditive Model for Spatial Disaggregation
• Implementing Approximate Bayesian Inference using Adaptive Quadrature: the aghq Package

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