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On Data-Driven Management of the COVID-19 Outbreak in South Africa (lay summary)

This is a lay summary of the article published under the DOI: 10.1101/2020.04.07.20057133

Published onJul 03, 2023
On Data-Driven Management of the COVID-19 Outbreak in South Africa (lay summary)

Computer model tracked early COVID-19 spread in South Africa 

In 2020, researchers used a computer model to predict how COVID-19 would spread in South Africa. They found that it was likely that South Africa was only at the beginning stages of the pandemic, and up to 18 000 beds may have been needed for the first peak.

COVID-19 spread rapidly early in 2020 to become a global pandemic. African health systems were already under pressure in many places, so African governments and doctors needed to plan for managing and limiting the spread of COVID-19.

One way to help decision-makers was to give them data and predictions of how COVID-19 might spread. 

Researchers thus used South African, Chinese, and Italian data to update a commonly-used computer model. The model predicted how South Africans might go from being susceptible, to infected, to recovered from, and immune to, COVID-19.

They predicted that the peak number of infections would occur 31 to 71 days after the start of the first wave. They also predicted that South Africa would need 7000 to 18000 general hospital beds and 2350 to 6000 intensive care beds at the first peak.

Their models suggested that on 15 April 2020, there were two most likely future scenarios. The first was that South Africa was still in the early stages of the first wave. And the second was that South Africa’s unique demographic and response were very effective at limiting COVID-19.

The researchers provided the first model of COVID-19 for South Africa, which was thought to be a great tool to help governments and doctors make decisions on how to address COVID-19.

They suggested that people closely monitor resources, such as available hospital beds and COVID-19 infections. Then, governments could compare these numbers and model predictions to guide them in knowing how strongly to limit COVID-19 spread.

The researchers caution that they used their own judgement when updating some parts of the model. They said that future studies should use other models to do so instead. Future studies could also take conditions in South Africa, such as high rates of immune disease and densely populated informal settlements, into account when making models.


The rapid spread of the novel coronavirus (SARS-CoV-2) has highlighted the need for the development of rapid mitigating responses under conditions of extreme uncertainty. While numerous works have provided projections of the progression of the pandemic, very little work has been focused on its progression in Africa and South Africa, in particular. In this work, we calibrate the susceptible-infected-recovered (SIR) compartmental model to South African data using initial conditions inferred from progression in Hubei, China and Lombardy, Italy. The results suggest two plausible hypotheses - either the COVID-19 pandemic is still at very early stages of progression in South Africa or a combination of prompt mitigating measures, demographics and social factors have resulted in a slowdown in its spread and severity. We further propose pandemic monitoring and health system capacity metrics for assisting decision-makers in evaluating which of the two hypotheses is most probable.


This summary is a free resource intended to make African research and research that affects Africa, more accessible to non-expert global audiences. It was compiled by ScienceLink's team of professional African science communicators as part of the Masakhane MT: Decolonise Science project. ScienceLink has taken every precaution possible during the writing, editing, and fact-checking process to ensure that this summary is easy to read and understand, while accurately reporting on the facts presented in the original research paper. Note, however, that this summary has not been fact-checked or approved by the authors of the original research paper, so this summary should be used as a secondary resource. Therefore, before using, citing or republishing this summary, please verify the information presented with the original authors of the research paper, or email [email protected] for more information.

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