Python, A. et al. (2022) A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185(1), pp. 202-218. (doi: 10.1111/rssa.12738) (PMID:34908651) (PMCID:PMC8662135)
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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.
| Item Type: | Articles |
|---|---|
| Additional Information: | AP has been funded by Zhejiang University (Educational Funding Grant No. 2020XGZX054, Global Partnership Fund Grant No. 188170-11103, and Fundamental Research Funds for the Central Universities Grant No. 2021QN81029). JY has been funded by the National Natural Science Foundation of China (Grant No. 61825205 and Grant No. 61772459). BL has been funded by the National Natural Science Foundation of China (Grant No. 41601001) and The Royal Society, United Kingdom (Grant No. NF171120). AB has been funded by the German Federal Ministry of Education and Research (BMBF) (Grant No. 01IS18036A). |
| Keywords: | COVID-19, downscaling, spatially disaggregated data. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Illian, Professor Janine |
| Authors: | Python, A., Bender, A., Blangiardo, M., Illian, J. B., Lin, Y., Liu, B., Lucas, T. C.D., Tan, S., Wen, Y., Svanidze, D., and Yin, J. |
| College/School: | College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| Journal Name: | Journal of the Royal Statistical Society: Series A (Statistics in Society) |
| Publisher: | Wiley |
| ISSN: | 0964-1998 |
| ISSN (Online): | 1467-985X |
| Published Online: | 15 September 2021 |
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