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.
disaggregation: An R Package for Bayesian Spatial Disaggregation Modelling
1 Pith paper cite this work, alongside 6 external citations. Polarity classification is still indexing.
1
Pith paper citing it
6
external citations · Pith
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.
fields
stat.AP 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
A Bayesian Spatio-Temporal Top-Down Framework for Estimating Opioid Use Disorder Risk Under Data Sparsity
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.