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disaggregation: An R Package for Bayesian Spatial Disaggregation Modelling

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arxiv 2001.04847 v1 pith:K2YF3NJ7 submitted 2020-01-09 stat.CO stat.ME

Anita K. Nandi , Tim C. D. Lucas , Rohan Arambepola , Peter Gething , Daniel J. Weiss This is my paper · ORCID
classification stat.COstat.ME
keywords disaggregationdatafine-scalemodellingaggregatedlargepackagepredictions
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 6 citations worldwide. Full citation record

  1. A Bayesian Spatio-Temporal Top-Down Framework for Estimating Opioid Use Disorder Risk Under Data Sparsity

    stat.AP 2025-06 conditional novelty 6.0 of 10

    A two-stage Bayesian downscaling model estimates county-level opioid use disorder risk from state-level survey counts, but its validation uses data generated by the model itself and shows large county-level errors.

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