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

• Published in: Computational and mathematical methods in medicine 2020, Vol.2020 (2020), p.1-12
• Main Authors: Visaya, M. V., Chukwu, C. W., Chirove, F., Nyabadza, F.
• Format: Article
• Language: English
• Subjects: Computational Biology; Computer Simulation; COVID-19 - epidemiology; COVID-19 - prevention & control; COVID-19 - transmission; Emigration and Immigration - statistics & numerical data; Humans; Mathematical Concepts; Models, Biological; Pandemics - prevention & control; Pandemics - statistics & numerical data; Physical Distancing; Quarantine - statistics & numerical data; South Africa - epidemiology
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2020/5379278

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.