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Quantifying early COVID-19 outbreak transmission in South Africa and exploring vaccine efficacy scenarios

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  • Zindoga Mukandavire
  • Farai Nyabadza
  • Noble J Malunguza
  • Diego F Cuadros
  • Tinevimbo Shiri
  • Godfrey Musuka

Abstract

The emergence and fast global spread of COVID-19 has presented one of the greatest public health challenges in modern times with no proven cure or vaccine. Africa is still early in this epidemic, therefore the extent of disease severity is not yet clear. We used a mathematical model to fit to the observed cases of COVID-19 in South Africa to estimate the basic reproductive number and critical vaccination coverage to control the disease for different hypothetical vaccine efficacy scenarios. We also estimated the percentage reduction in effective contacts due to the social distancing measures implemented. Early model estimates show that COVID-19 outbreak in South Africa had a basic reproductive number of 2.95 (95% credible interval [CrI] 2.83–3.33). A vaccine with 70% efficacy had the capacity to contain COVID-19 outbreak but at very higher vaccination coverage 94.44% (95% Crl 92.44–99.92%) with a vaccine of 100% efficacy requiring 66.10% (95% Crl 64.72–69.95%) coverage. Social distancing measures put in place have so far reduced the number of social contacts by 80.31% (95% Crl 79.76–80.85%). These findings suggest that a highly efficacious vaccine would have been required to contain COVID-19 in South Africa. Therefore, the current social distancing measures to reduce contacts will remain key in controlling the infection in the absence of vaccines and other therapeutics.

Suggested Citation

  • Zindoga Mukandavire & Farai Nyabadza & Noble J Malunguza & Diego F Cuadros & Tinevimbo Shiri & Godfrey Musuka, 2020. "Quantifying early COVID-19 outbreak transmission in South Africa and exploring vaccine efficacy scenarios," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-11, July.
    • Handle: RePEc:plo:pone00:0236003
    DOI: 10.1371/journal.pone.0236003
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    References listed on IDEAS

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    1. Soetaert, Karline & Petzoldt, Thomas, 2010. "Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i03).
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    1. Gilberto Gonzalez-Parra & Abraham J. Arenas, 2021. "Nonlinear Dynamics of the Introduction of a New SARS-CoV-2 Variant with Different Infectiousness," Mathematics, MDPI, vol. 9(13), pages 1-22, July.
    2. Nikolaos P. Rachaniotis & Thomas K. Dasaklis & Filippos Fotopoulos & Platon Tinios, 2021. "A Two-Phase Stochastic Dynamic Model for COVID-19 Mid-Term Policy Recommendations in Greece: A Pathway towards Mass Vaccination," IJERPH, MDPI, vol. 18(5), pages 1-21, March.
    3. Naudé, Wim & Cameron, Martin, 2020. "Failing to Pull Together: South Africa's Troubled Response to COVID-19," IZA Discussion Papers 13649, Institute of Labor Economics (IZA).
    4. Hannah Al Ali & Alireza Daneshkhah & Abdesslam Boutayeb & Zindoga Mukandavire, 2022. "Examining Type 1 Diabetes Mathematical Models Using Experimental Data," IJERPH, MDPI, vol. 19(2), pages 1-20, January.
    5. Yongin Choi & James Slghee Kim & Jung Eun Kim & Heejin Choi & Chang Hyeong Lee, 2021. "Vaccination Prioritization Strategies for COVID-19 in Korea: A Mathematical Modeling Approach," IJERPH, MDPI, vol. 18(8), pages 1-19, April.
    6. Emanuele Blasioli & Bahareh Mansouri & Srinivas Subramanya Tamvada & Elkafi Hassini, 2023. "Vaccine Allocation and Distribution: A Review with a Focus on Quantitative Methodologies and Application to Equity, Hesitancy, and COVID-19 Pandemic," SN Operations Research Forum, Springer, vol. 4(2), pages 1-32, June.
    7. Tanmoy Bhowmik & Sudipta Dey Tirtha & Naveen Chandra Iraganaboina & Naveen Eluru, 2021. "A comprehensive analysis of COVID-19 transmission and mortality rates at the county level in the United States considering socio-demographics, health indicators, mobility trends and health care infras," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.

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