Sample-pooling strategy for SARS-CoV-2 detection among students and staff of the university of Sannio

TLDR: In this paper, a simple monodimensional sample pooling strategy was used to detect SARS-CoV-2 positive cases in a community of asymptomatic individuals with minor requirements of reagents and time when compared to normal testing procedures.

Abstract: Since the beginning of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, it has been clear that testing large groups of the population was the key to stem infection and prevent the effects of the coronavirus disease of 2019, mostly among sensitive patients. On the other hand, time and cost-sustainability of virus detection by molecular analysis such as reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) may be a major issue if testing is extended to large communities, mainly asymptomatic large communities. In this context, sample-pooling and test grouping could offer an effective solution. Here we report the screening on 1195 oral-nasopharyngeal swabs collected from students and staff of the Universita degli Studi del Sannio (University of Sannio, Benevento, Campania, Italy) and analyzed by an in-house developed multiplex RT-qPCR for SARS-CoV-2 detection through a simple monodimensional sample pooling strategy. Overall, 400 distinct pools were generated and, within 24 h after swab collection, five positive samples were identified. Out of them, four were confirmed by using a commercially available kit suitable for in vitro diagnostic use (IVD). High accuracy, sensitivity and specificity were also determined by comparing our results with a reference IVD assay for all deconvoluted samples. Overall, we conducted 463 analyses instead of 1195, reducing testing resources by more than 60% without lengthening diagnosis time and without significant losses in sensitivity, suggesting that our strategy was successful in recognizing positive cases in a community of asymptomatic individuals with minor requirements of reagents and time when compared to normal testing procedures.