“Group testing as a strategy for COVID-19 epidemiological monitoring and community surveillance”
Invented in 1943 by an American statistician, Robert Dorfman, the principle of group testing is simple: rather than testing one hundred samples, group them into ten groups (called pools) of ten samples and test each group. If one of the groups is positive, then at least one of the samples contains the infectious agent. Conversely, if the pool is negative, then it can be concluded that each specimen in the pool must be negative as well (at least, assuming that there are no false negatives, i.e., a negative result when an individual is carrying the virus). In a recent article (Brault et al. PLOS Comp. Biology 2021), we propose an analysis and applications of sample pooling to the epidemiologic monitoring of COVID-19 with a focus on the prevention of epidemic outbreak in closed connected communities (e.g. K12 schools, universities or nursing homes). Our results are based on a model for the molecular tests for the presence of virus in a sample (e.g. RT-qPCR); our estimation for the risk of false-negatives is built upon a statistical analysis tool (censored-Gaussian) applied to large-scale clinical datasets on the viral load of infected individuals.