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

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 dataset. The results highlight discrepancies in the counts of coronavirus-infected cases at district level and identify districts that may require further investigation. Funding Statement: This work was supported by Zhejiang University Educational Funding (2020XGZX054), the National Natural Science Foundation of China under Grants (61825205 and 41601001), and The Royal Society, United Kingdom (NF171120). Declaration of Interests: The authors declare no competing interests.