A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales.
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ABSTRACT:
SUBMITTER: Python A
PROVIDER: S-EPMC8662135 | biostudies-literature | 2021 Sep
REPOSITORIES: biostudies-literature
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A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales.
Python Andre A Bender Andreas A Blangiardo Marta M Illian Janine B JB Lin Ying Y Liu Baoli B Lucas Tim C D TCD Tan Siwei S Wen Yingying Y Svanidze Davit D Yin Jianwei J
Journal of the Royal Statistical Society. Series A, (Statistics in Society) 20210915 1
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 ...[more]
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