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Bayesian inference of COVID-19 spreading rates in South Africa

This is a lay summary of the article published under the DOI: 10.1371/journal.pone.0237126

Published onApr 19, 2023
Bayesian inference of COVID-19 spreading rates in South Africa

South Africa’s first lockdown likely reduced COVID-19 spread by 80%

Many researchers made tools to help people decide how to combat COVID-19. These researchers found that the South African government's lockdown was very effective and slowed the spread of the disease by 80%.

On 11 March 2020, the World Health Organisation declared that the world was in a pandemic due to the SARS-COV-2 virus. Therefore, governments worldwide needed to act quickly to stop infections and minimise deaths.

These researchers wanted to create a model that tracked how COVID-19 spread over time, and how well government's actions to stop the spread worked. They also wanted to predict the number of infections and the demand for health care.

To create statistical models, the researchers used publicly available COVID-19 data from South Africa up to 20 April 2020. Their models used data to investigate how a person goes through the stages of the disease - they learned to predict when a person might go from being exposed, to infectious, and recovered.

They learned 2 things about COVID-19 in this way. First, the average amount of time between someone catching COVID-19 and being able to infect others was 4.5 days. Second, people were  able to infect others for about 6.6 days after being infected themselves.

The researchers saw that the lockdown in South Africa, including a travel ban and school closures, decreased the spread of COVID-19 by 80%. They also saw that mass testing seemed to increase the spread slightly. But, they suggested that this is because mass testing picks up infections that would otherwise go unnoticed, so this might in fact be tracking the spread more accurately.

The results of this study were similar to previous ones.

The researchers said they plan to analyse the spread of COVID-19 per region instead of the country as a whole. They also noted that they might need to repeat the study after the government takes new actions to limit the disease.


The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for performing accurate inference with limited data. Fundamental to the design of rapid state responses is the ability to perform epidemiological model parameter inference for localised trajectory predictions. In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The first change point coincides with state enactment of a travel ban and the resultant containment of imported infections. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. The results further suggest that due to the likely effect of the national lockdown, community level transmissions are slower than the original imported case driven spread of the disease.


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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