A Downscaling Approach to Compare COVID-19 Count Data from Databases Aggregated at Different Spatial Scales. (15th September 2021)

• Record Type: Journal Article
• Authors: Python, Andre; Bender, Andreas; Blangiardo, Marta; Illian, Janine B.; Lin, Ying; Liu, Baoli; Lucas, Tim C.D.; Tan, Siwei; Wen, Yingying; Svanidze, Davit; Yin, Jianwei
• Is Part Of: Journal of the Royal Statistical Society. Volume 185:Number 1(2022)
• Page Start: 202
• Page End: 218
• Publication Date: 2021-09-15
• Subjects: COVID-19 -- downscaling -- spatially disaggregated data; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12738
• Languages: English
• ISSNs: 0964-1998

Abstract

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.