New Quantitative Strategy for Pooling COVID-19 Tests Could Detect Outbreaks Early

If the test result for a group is positive, then at least one of the group’s samples contains the infectious agent. Conversely, a negative result should, in principle, indicate that none of the combined samples contains the infectious agent. Sample pooling method can reduce the cost can accelerate screening campaign, yet pooling can result in a false positive result whenever mixing samples involves a too large dilution - namely when the quantity of the virus in the tested sample falls below the test’s level of detection.

To assess the efficiency of screening based on a group testing strategy, the researchers who are members of the MODCOV19 platform have developed a mathematical model that estimates this dilution effect based on sample group size. Their model can quantify the reduction of sensitivity for tests, hence the capacity of tests with larger or smaller groups to detect the presence of a contagious individual. Unlike methods that try to minimize the number of tests needed to establish an individual diagnostic for the contaminated individual or individuals, the model’s central aim is to help optimize a collective screening strategy: how best to optimize group size in order to detect a maximum number of individuals all while limiting the risk of false negatives?

The researchers also present an accurate method for measuring the proportion of individuals infected in the tested population (known as prevalence) which can guide the application of preventive measures against epidemic risk. According to their study, group testing is of particular interest because it can quickly and regularly evaluate the presence of SARS-COV-2 within “closed” communities (such as nursing homes or university residences).

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