Coronavirus tests ‘could be more efficient using’ this mathematical technique
- Algorithm was able to pinpoint origin of positive cases in a study by Israeli scientists that pooled samples
- Testing groups rather than individuals can improve effectiveness of Covid-19 testing process
Researchers from Ben-Gurion University of the Negev in Israel adopted a new approach to a technique known as pool-sampling, which tests a group of people rather than individuals to identify suspected carriers quickly at a relatively low cost.
An algorithm programmed to use combination, a mathematical technique that deals with the selection of items from a collection, was able to identify cases with a 100 per cent accuracy rate, according to the research published in Science Advances last week.
The team divided 384 samples into 48 pools, and the content of each sample was mixed to ensure it appeared in six different pools.
Four positive cases were found among the samples and scientists were able to use the technique to pinpoint where it had come from.
About 10 to 30 per cent of Covid-19 patients had no symptoms but could spread the virus significantly, according to the lead researcher Angel Porgador.
“Until there is a vaccine, there will be an urgent need to increase diagnostic testing capabilities to allow for screening of asymptomatic and pre-symptomatic populations,” he said in a statement.
Pool sampling is already being used as a weapon in the global fight against the pandemic. Beijing, for instance, screened 10 million residents in about a week using this method.