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Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective
In late December 2019, strange pneumonia was detected in a seafood market in Wuhan, China which was later termed COVID-19 by the World Health Organization. At present, the virus has spread across 232 countries worldwide killing 2,409,011 as of 17 February 2021 (9:37 CET). Motivated by a recent datas...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8067784/ https://www.ncbi.nlm.nih.gov/pubmed/33935350 http://dx.doi.org/10.1007/s10708-021-10427-0 |
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author | Appiah-Otoo, Isaac Kursah, Matthew Biniyam |
author_facet | Appiah-Otoo, Isaac Kursah, Matthew Biniyam |
author_sort | Appiah-Otoo, Isaac |
collection | PubMed |
description | In late December 2019, strange pneumonia was detected in a seafood market in Wuhan, China which was later termed COVID-19 by the World Health Organization. At present, the virus has spread across 232 countries worldwide killing 2,409,011 as of 17 February 2021 (9:37 CET). Motivated by a recent dataset, knowledge gaps, surge in global cases, and the need to combat the virus spread, this study examined the relationship between COVID-19 confirmed cases and attributable deaths at the global and regional levels. We used a panel of 232 countries (further disaggregated into Africa-49, Americas-54, Eastern Mediterranean-23, Europe-61, Southeast Asia-10, and Western Pacific-35) from 03 January 2020 to 28 November 2020, and the instrumental variable generalized method of moment’s model (IV-GMM) for analysing the datasets. The results showed that COVID-19 confirmed cases at both the global and regional levels have a strong positive effect on deaths. Thus, the confirmed cases significantly increase attributable deaths at the global and regional levels. At the global level, a 1% increase in confirmed cases increases attributable deaths by 0.78%. Regionally, a 1% increase in confirmed cases increases attributable deaths by 0.65% in Africa, 0.90% in the Americas, 0.67% in the Eastern Mediterranean, 0.72% in Europe, 0.88% in Southeast Asia, and 0.52% in the Western Pacific. This study expands the understanding of the relationship between COVID-19 cases and deaths by using a global dataset and the instrumental variable generalized method of moment’s model (IV-GMM) for the analysis that addresses endogeneity and omitted variable issues. |
format | Online Article Text |
id | pubmed-8067784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-80677842021-04-26 Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective Appiah-Otoo, Isaac Kursah, Matthew Biniyam GeoJournal Article In late December 2019, strange pneumonia was detected in a seafood market in Wuhan, China which was later termed COVID-19 by the World Health Organization. At present, the virus has spread across 232 countries worldwide killing 2,409,011 as of 17 February 2021 (9:37 CET). Motivated by a recent dataset, knowledge gaps, surge in global cases, and the need to combat the virus spread, this study examined the relationship between COVID-19 confirmed cases and attributable deaths at the global and regional levels. We used a panel of 232 countries (further disaggregated into Africa-49, Americas-54, Eastern Mediterranean-23, Europe-61, Southeast Asia-10, and Western Pacific-35) from 03 January 2020 to 28 November 2020, and the instrumental variable generalized method of moment’s model (IV-GMM) for analysing the datasets. The results showed that COVID-19 confirmed cases at both the global and regional levels have a strong positive effect on deaths. Thus, the confirmed cases significantly increase attributable deaths at the global and regional levels. At the global level, a 1% increase in confirmed cases increases attributable deaths by 0.78%. Regionally, a 1% increase in confirmed cases increases attributable deaths by 0.65% in Africa, 0.90% in the Americas, 0.67% in the Eastern Mediterranean, 0.72% in Europe, 0.88% in Southeast Asia, and 0.52% in the Western Pacific. This study expands the understanding of the relationship between COVID-19 cases and deaths by using a global dataset and the instrumental variable generalized method of moment’s model (IV-GMM) for the analysis that addresses endogeneity and omitted variable issues. Springer Netherlands 2021-04-24 2022 /pmc/articles/PMC8067784/ /pubmed/33935350 http://dx.doi.org/10.1007/s10708-021-10427-0 Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Appiah-Otoo, Isaac Kursah, Matthew Biniyam Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective |
title | Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective |
title_full | Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective |
title_fullStr | Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective |
title_full_unstemmed | Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective |
title_short | Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective |
title_sort | modelling spatial variations of novel coronavirus disease (covid-19): evidence from a global perspective |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8067784/ https://www.ncbi.nlm.nih.gov/pubmed/33935350 http://dx.doi.org/10.1007/s10708-021-10427-0 |
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