SA scientists use maths to predict Covid-19 outcomes

CAPE TOWN- South African scientists and researchers have been developing mathematical models to understand and investigate how Covid-19 might progress in the country and how it could affect the population’s health.

Senior lecturer at the University of Cape Town (UCT) and member of the South African Covid-19 Modelling Consortium, Dr Sheetal Silal said that by using mathematics, they can portray how a disease behaves and learn new things about its epidemiology.

Silal shared in an article on the World Economic Forum, that while mathematical disease modelling cannot predict exact outcomes, it is a useful tool in understanding the range of possible futures, given a set of assumptions. 

“Our purpose is to provide this information to the government in a way that is useful when making policy decisions,” she said. 

In order to arrive at some estimated Covid-19 outcomes, Silal said that she gathers large amounts of data on the virus, such as infectivity, recovery time, mortality rates, the behaviour of people and if they observe physical distancing and lockdown measures, the capacity of the healthcare system, and how many intensive care units there are. 

After combining the data, she is able to show how the virus could spread, what effect it would have on the health infrastructure and the potential impact of intervention strategies.

Covid-19 modelling could look different in each country and other countries’ data will not necessarily help to inform South African models. Silal said that small differences in testing capabilities and population average age could lead to divergent results. 

The Coronavirus has shown to affect the older population, she said, many African countries have younger populations than European countries. This is why Silal said she would not consider transplanting European data into an African context without first taking age into account.

In the South African context, as the country is considered a low- and middle-income country (LMIC), government policy decisions cannot be purely health-based, or purely model-based. She says economic and social factors need to be integrated into decision-making. 

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