A Downscaling Approach to Compare COVID-19 Count Data from Databases Aggregated at Different Spatial Scales

  1. Andre Python
  2. Andreas Bender
  3. Marta Blangiardo
  4. Janine B. Illian
  5. Ying Lin
  6. Baoli Liu
  7. Tim C.D. Lucas
  8. Siwei Tan
  9. Yingying Wen
  10. Davit Svanidze
  11. Jianwei Yin

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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.

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  1. Version published to 10.1111/rssa.12738
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  3. Version published to 10.1101/2020.06.17.20133959v1 on medRxiv