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Modelling spatial variations of coronavirus disease (COVID-19) in Africa
Clinical and epidemiological evidence has been advanced for human-to-human transmission of the novel coronavirus rampaging the world since late 2019. Outliers in the human-to-human transmission are yet to be explored. In this study, we examined the spatial density and leaned statistical credence to...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195380/ https://www.ncbi.nlm.nih.gov/pubmed/32361455 http://dx.doi.org/10.1016/j.scitotenv.2020.138998 |
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author | Adekunle, Ibrahim Ayoade Onanuga, Abayomi Toyin Akinola, Olanrewaju Olugbenga Ogunbanjo, Olakitan Wahab |
author_facet | Adekunle, Ibrahim Ayoade Onanuga, Abayomi Toyin Akinola, Olanrewaju Olugbenga Ogunbanjo, Olakitan Wahab |
author_sort | Adekunle, Ibrahim Ayoade |
collection | PubMed |
description | Clinical and epidemiological evidence has been advanced for human-to-human transmission of the novel coronavirus rampaging the world since late 2019. Outliers in the human-to-human transmission are yet to be explored. In this study, we examined the spatial density and leaned statistical credence to the global debate. We constructed spatial variations of clusters that examined the nexus between COVID-19 attributable deaths and confirmed cases. We rely on publicly available data on confirmed cases and death across Africa to unravel the unobserved factors, that could be responsible for the spread of COVID-19. We relied on the dynamic system generalised method of moment estimation procedure and found a ~0.045 Covid19 deaths as a result of confirmed cases in Africa. We accounted for cross-sectional dependence and found a basis for the strict orthogonal relationship. Policy measures were discussed. |
format | Online Article Text |
id | pubmed-7195380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71953802020-05-02 Modelling spatial variations of coronavirus disease (COVID-19) in Africa Adekunle, Ibrahim Ayoade Onanuga, Abayomi Toyin Akinola, Olanrewaju Olugbenga Ogunbanjo, Olakitan Wahab Sci Total Environ Article Clinical and epidemiological evidence has been advanced for human-to-human transmission of the novel coronavirus rampaging the world since late 2019. Outliers in the human-to-human transmission are yet to be explored. In this study, we examined the spatial density and leaned statistical credence to the global debate. We constructed spatial variations of clusters that examined the nexus between COVID-19 attributable deaths and confirmed cases. We rely on publicly available data on confirmed cases and death across Africa to unravel the unobserved factors, that could be responsible for the spread of COVID-19. We relied on the dynamic system generalised method of moment estimation procedure and found a ~0.045 Covid19 deaths as a result of confirmed cases in Africa. We accounted for cross-sectional dependence and found a basis for the strict orthogonal relationship. Policy measures were discussed. Elsevier B.V. 2020-08-10 2020-04-26 /pmc/articles/PMC7195380/ /pubmed/32361455 http://dx.doi.org/10.1016/j.scitotenv.2020.138998 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Adekunle, Ibrahim Ayoade Onanuga, Abayomi Toyin Akinola, Olanrewaju Olugbenga Ogunbanjo, Olakitan Wahab Modelling spatial variations of coronavirus disease (COVID-19) in Africa |
title | Modelling spatial variations of coronavirus disease (COVID-19) in Africa |
title_full | Modelling spatial variations of coronavirus disease (COVID-19) in Africa |
title_fullStr | Modelling spatial variations of coronavirus disease (COVID-19) in Africa |
title_full_unstemmed | Modelling spatial variations of coronavirus disease (COVID-19) in Africa |
title_short | Modelling spatial variations of coronavirus disease (COVID-19) in Africa |
title_sort | modelling spatial variations of coronavirus disease (covid-19) in africa |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195380/ https://www.ncbi.nlm.nih.gov/pubmed/32361455 http://dx.doi.org/10.1016/j.scitotenv.2020.138998 |
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