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Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa.
Math Biosci. 2020 10; 328:108441.MB

Abstract

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.

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Authors+Show Affiliations

Garba SM
Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria 0002, South Africa.
Lubuma JM
Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria 0002, South Africa. Electronic address: Jean.Lubuma@up.ac.za.
Tsanou B
Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria 0002, South Africa; Department of Mathematics and Computer Sciences, University of Dschang, P.O. Box 96 Dschang, Cameroon.

MeSH

Basic Reproduction NumberBetacoronavirusCOVID-19COVID-19 TestingClinical Laboratory TechniquesComputer SimulationContact TracingCoronavirus InfectionsEnvironmental MicrobiologyEpidemiological MonitoringHumansMathematical ConceptsModels, BiologicalPandemicsPneumonia, ViralQuarantineSARS-CoV-2South AfricaTime Factors

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32763338

Citation

Garba, Salisu M., et al. "Modeling the Transmission Dynamics of the COVID-19 Pandemic in South Africa." Mathematical Biosciences, vol. 328, 2020, p. 108441.
Garba SM, Lubuma JM, Tsanou B. Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa. Math Biosci. 2020;328:108441.
Garba, S. M., Lubuma, J. M., & Tsanou, B. (2020). Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa. Mathematical Biosciences, 328, 108441. https://doi.org/10.1016/j.mbs.2020.108441
Garba SM, Lubuma JM, Tsanou B. Modeling the Transmission Dynamics of the COVID-19 Pandemic in South Africa. Math Biosci. 2020;328:108441. PubMed PMID: 32763338.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa. AU - Garba,Salisu M, AU - Lubuma,Jean M-S, AU - Tsanou,Berge, Y1 - 2020/08/04/ PY - 2020/06/16/received PY - 2020/07/30/revised PY - 2020/08/02/accepted PY - 2020/8/9/pubmed PY - 2020/9/25/medline PY - 2020/8/9/entrez KW - COVID-19 KW - Control reproduction number KW - Environmental contamination KW - Isolation KW - Social-distancing SP - 108441 EP - 108441 JF - Mathematical biosciences JO - Math Biosci VL - 328 N2 - 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. SN - 1879-3134 UR - https://www.unboundmedicine.com/medline/citation/32763338/Modeling_the_transmission_dynamics_of_the_COVID_19_Pandemic_in_South_Africa_ DB - PRIME DP - Unbound Medicine ER -