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Title: | Analyzing the GHSI puzzle of whether highly developed countries fared worse in COVID-19 | Authors: | Marković, Sofija Salom, Igor Rodić, Anđela Đorđević, Marko |
Keywords: | Machine learning;Viral infection | Issue Date: | 21-Oct-2022 | Rank: | M21 | Publisher: | National Library of Medicine | Citation: | Markovic, S., Salom, I., Rodic, A. et al. Analyzing the GHSI puzzle of whether highly developed countries fared worse in COVID-19. Sci Rep 12, 17711 (2022). https://doi.org/10.1038/s41598-022-22578-2. | Journal: | Scientific Reports | Volume: | 12 | Issue: | 1 | Start page: | 17711 | Abstract: | Global Health Security Index (GHSI) categories are formulated to assess the capacity of world countries to deal with infectious disease risks. Thus, higher values of these indices were expected to translate to lower COVID-19 severity. However, it turned out to be the opposite, surprisingly suggesting that higher estimated country preparedness to epidemics may lead to higher disease mortality. To address this puzzle, we: (i) use a model-derived measure of COVID-19 severity; (ii) employ a range of statistical learning approaches, including non-parametric machine learning methods; (iii) consider the overall excess mortality, in addition to official COVID-19 fatality counts. Our results suggest that the puzzle is, to a large extent, an artifact of oversimplified data analysis and a consequence of misclassified COVID-19 deaths, combined with the higher median age of the population and earlier epidemics onset in countries with high GHSI scores. |
URI: | https://biore.bio.bg.ac.rs/handle/123456789/5151 | ISSN: | 2045-2322 | DOI: | 10.1038/s41598-022-22578-2 |
Appears in Collections: | Journal Article |
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