A Downscaling Approach to Compare COVID-19 Count Data from Databases Aggregated at Different Spatial Scales
该研究利用中国2020年1-2月数据，通过贝叶斯降尺度回归模型将省级数据降尺度到市级，比较了不同空间尺度下的COVID-19病例计数差异，并识别出需要进一步调查的地区。
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.