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A downscaling approach to compare COVID‐19 count data from databases aggregated at different spatial scales

Author

Listed:
  • Andre Python
  • Andreas Bender
  • Marta Blangiardo
  • Janine B. Illian
  • Ying Lin
  • Baoli Liu
  • Tim C.D. Lucas
  • Siwei Tan
  • Yingying Wen
  • Davit Svanidze
  • Jianwei Yin

Abstract

As the COVID‐19 pandemic continues to threaten various regions around the world, obtaining accurate and reliable COVID‐19 data is crucial for governments and local communities aiming at rigorously assessing the extent and magnitude of the virus spread and deploying efficient interventions. Using data reported between January and February 2020 in China, we compared counts of COVID‐19 from near‐real‐time spatially disaggregated data (city level) with fine‐spatial scale predictions from a Bayesian downscaling regression model applied to a reference province‐level data set. The results highlight discrepancies in the counts of coronavirus‐infected cases at the district level and identify districts that may require further investigation.

Suggested Citation

  • Andre Python & Andreas Bender & Marta Blangiardo & Janine B. Illian & Ying Lin & Baoli Liu & Tim C.D. Lucas & Siwei Tan & Yingying Wen & Davit Svanidze & Jianwei Yin, 2022. "A downscaling approach to compare COVID‐19 count data from databases aggregated at different spatial scales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 202-218, January.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:1:p:202-218
    DOI: 10.1111/rssa.12738
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    References listed on IDEAS

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