Modelling the Potential Impact of Social Distancing on the COVID-19 Epidemic in South Africa

  1. F. Nyabadza
  2. F. Chirove
  3. C. W. Chukwu
  4. M. V. Visaya

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Abstract

The novel coronavirus (COVID-19) pandemic continues to be a global health problem whose impact has been significantly felt in South Africa. With the global spread increasing and infecting millions, containment efforts by countries have largely focused on lockdowns and social distancing to minimise contact between persons. Social distancing has been touted as the best form of response in managing a rapid increase in the number of infected cases. In this paper, we present a deterministic model to describe the impact of social distancing on the transmission dynamics of COVID-19 in South Africa. The model is fitted to data from March 5 to April 13, 2020, on the cumulative number of infected cases, and a scenario analysis on different levels of social distancing is presented. The model shows that with the levels of social distancing under the initial lockdown level between March 26 and April 13, 2020, there would be a projected continued rise in the number of infected cases. The model also looks at the impact of relaxing the social distancing measures after the initial announcement of the lockdown. It is shown that relaxation of social distancing by 2% can result in a 23% rise in the number of cumulative cases whilst an increase in the level of social distancing by 2% would reduce the number of cumulative cases by about 18%. The model results accurately predicted the number of cases after the initial lockdown level was relaxed towards the end of April 2020. These results have implications on the management and policy direction in the early phase of the epidemic.

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    SciScore for 10.1101/2020.04.21.20074492: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
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    • No protocol registration statement was detected.

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  2. Version published to 10.1155/2020/5379278
  3. ScreenIT

    SciScore for 10.1101/2020.04.21.20074492: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Day Date Total cases 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 05-03-2020 06-03-2020 07-03-2020 08-03-2020 09-03-2020 10-03-2020 11-03-2020 12-03-2020 13-03-2020 14-03-2020 15-03-2020 16-03-2020 17-03-2020 18-03-2020 19-03-2020 20-03-2020 21-03-2020 22-03-2020 23-03-2020 24-03-2020 1 1 2 3 7 9 13 16 24 38 51 62 85 116 150 202 240 274 402 554 Increase in new cases (%) 0 100 50 133 29 44 23 50 58 34 22 37 36 29 35 19 14 47 38 Day Date Total cases 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 25-03-2020 26-03-2020 27-03-2020 28-03-2020 29-03-2020 30-03-2020 31-03-2020 01-04-2020 02-04-2020 03-04-2020 04-04-2020 05-04-2020 06-04-2020 07-04-2020 08-04-2020 09-04-2020 10-04-2020 11-04-2020 12-04-2020 13-04-2020 709 927 1170 1187 1280 1326 1353 1380 1462 1505 1585 1655 1686 1749 1845 1934 2003 2028 2173 2272 Increase in new cases (%) 28 31 26 1 8 4 2 2 6 3 5 4 2 4 5 5 4 1 7 5 We fit the model to the data recorded before the lockdown using the least squares curve fitting method in Matlab.
    Matlab
    suggested: (MATLAB, SCR_001622)

    Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  4. Version published to 10.1101/2020.04.21.20074492v1 on medRxiv