Local data key in South Africa's Covid-19 fight, says WHO

The World Health Organisation (WHO) has advised the South African government to monitor the spread of Covid-19 on a seven-day cycle to determine whether the country should move to a different level of lockdown.

In an interview with the Sunday Times this week, the organisation's Dr Ambrose Talisuna praised the government's management of the Covid-19 outbreak.

With the move to level 2, Talisuna said SA may need to go back and forth between levels to suppress transmission of the coronavirus.

"Our guidance has been that we might have to be in this scenario… where we open and close. That has to be evidence-based. If the trend numbers continue to go down and are sustained in all the provinces, then you can say we can ease more. That is why it is important to collate very good data from the municipal level and analyse [it] over time.

"We call it the seven-day moving average, where you look at the trends in several parameters. Some might be confirmed cases, death, hospitalisation, testing. Testing should, of course, be… high and the target must be reached," Talisuna said.

"Cases in this country appear to have peaked at around July, and for the past three weeks we've been seeing some decline. The provinces have different levels in terms of testing. But there are other ways to look [at it] - for example in Nigeria, there have been some studies that have looked at serology in the population, so at least we have a clue on the degree of transmission that may be going on there."

The 43 WHO experts who arrived in SA last week will be here for an initial period of about three months.

Africa has fared relatively well in terms of the pandemic, defying what Talisuna called "doomsday predictions".

"We have not seen anywhere in Africa the numbers we thought we might, according to the models based on the worst-case scenario. It was doomsday predictions because there was not enough data to work with, so we were working on a lot of assumptions. The parameters were completely different.

"We looked at things like population density and predicted that in places where we have high population density we would have high [infection] numbers. Those were the initial parameter we used for the models, but as we get new information we adjust the models accordingly. Now we have some data which has to do with the outbreak itself, we now have a recovery rate and can put that into the models so they will be more realistic."

He said that though the models may not have been entirely accurate, they helped in terms of planning, with infrastructure such as high-flow oxygen being prioritised and wards being repurposed.