Cargando…

Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa

Since its emergence late in 2019, the COVID-19 pandemic continues to exude major public health and socio-economic burden globally. South Africa is currently the epicenter for the pandemic in Africa. This study is based on the use of a compartmental model to analyze the transmission dynamics of the d...

Descripción completa

Detalles Bibliográficos
Autores principales: Garba, Salisu M., Lubuma, Jean M.-S., Tsanou, Berge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7402282/
https://www.ncbi.nlm.nih.gov/pubmed/32763338
http://dx.doi.org/10.1016/j.mbs.2020.108441
_version_ 1783566724226875392
author Garba, Salisu M.
Lubuma, Jean M.-S.
Tsanou, Berge
author_facet Garba, Salisu M.
Lubuma, Jean M.-S.
Tsanou, Berge
author_sort Garba, Salisu M.
collection PubMed
description Since its emergence late in 2019, the COVID-19 pandemic continues to exude major public health and socio-economic burden globally. South Africa is currently the epicenter for the pandemic in Africa. This study is based on the use of a compartmental model to analyze the transmission dynamics of the disease in South Africa. A notable feature of the model is the incorporation of the role of environmental contamination by COVID-infected individuals. The model, which is fitted and parametrized using cumulative mortality data from South Africa, is used to assess the impact of various control and mitigation strategies. Rigorous analysis of the model reveals that its associated continuum of disease-free equilibria is globally-asymptotically stable whenever the control reproduction number is less than unity. The epidemiological implication of this result is that the disease will eventually die out, particularly if control measures are implemented early and for a sustainable period of time. For instance, numerical simulations suggest that if the lockdown measures in South Africa were implemented a week later than the 26 March, 2020 date it was implemented, this will result in the extension of the predicted peak time of the pandemic, and causing about 10% more cumulative deaths. In addition to illustrating the effectiveness of self-isolation in reducing the number of cases, our study emphasizes the importance of surveillance testing and contact tracing of the contacts and confirmed cases in curtailing the pandemic in South Africa.
format Online
Article
Text
id pubmed-7402282
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Elsevier Inc.
record_format MEDLINE/PubMed
spelling pubmed-74022822020-08-05 Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa Garba, Salisu M. Lubuma, Jean M.-S. Tsanou, Berge Math Biosci Article Since its emergence late in 2019, the COVID-19 pandemic continues to exude major public health and socio-economic burden globally. South Africa is currently the epicenter for the pandemic in Africa. This study is based on the use of a compartmental model to analyze the transmission dynamics of the disease in South Africa. A notable feature of the model is the incorporation of the role of environmental contamination by COVID-infected individuals. The model, which is fitted and parametrized using cumulative mortality data from South Africa, is used to assess the impact of various control and mitigation strategies. Rigorous analysis of the model reveals that its associated continuum of disease-free equilibria is globally-asymptotically stable whenever the control reproduction number is less than unity. The epidemiological implication of this result is that the disease will eventually die out, particularly if control measures are implemented early and for a sustainable period of time. For instance, numerical simulations suggest that if the lockdown measures in South Africa were implemented a week later than the 26 March, 2020 date it was implemented, this will result in the extension of the predicted peak time of the pandemic, and causing about 10% more cumulative deaths. In addition to illustrating the effectiveness of self-isolation in reducing the number of cases, our study emphasizes the importance of surveillance testing and contact tracing of the contacts and confirmed cases in curtailing the pandemic in South Africa. Elsevier Inc. 2020-10 2020-08-04 /pmc/articles/PMC7402282/ /pubmed/32763338 http://dx.doi.org/10.1016/j.mbs.2020.108441 Text en © 2020 Elsevier Inc. 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
Garba, Salisu M.
Lubuma, Jean M.-S.
Tsanou, Berge
Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa
title Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa
title_full Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa
title_fullStr Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa
title_full_unstemmed Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa
title_short Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa
title_sort modeling the transmission dynamics of the covid-19 pandemic in south africa
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7402282/
https://www.ncbi.nlm.nih.gov/pubmed/32763338
http://dx.doi.org/10.1016/j.mbs.2020.108441
work_keys_str_mv AT garbasalisum modelingthetransmissiondynamicsofthecovid19pandemicinsouthafrica
AT lubumajeanms modelingthetransmissiondynamicsofthecovid19pandemicinsouthafrica
AT tsanouberge modelingthetransmissiondynamicsofthecovid19pandemicinsouthafrica