Use of compressed sensing to expedite high-throughput diagnostic testing for COVID-19 and beyond

The rapid spread of SARS-CoV-2 has placed a significant burden on public health systems to provide swift and accurate diagnostic testing highlighting the critical need for innovative testing approaches for future pandemics. In this study, we present a novel sample pooling procedure based on compressed sensing theory to accurately identify virally infected patients at high prevalence rates utilizing an innovative viral RNA extraction process to minimize sample dilution. At prevalence rates ranging from 0–14.3%, the number of tests required to identify the infection status of all patients was reduced by 69.26% as compared to conventional testing in primary human SARS-CoV-2 nasopharyngeal swabs and a coronavirus model system. Our method provided quantification of individual sample viral load within a pool as well as a binary positive-negative result. Additionally, our modified pooling and RNA extraction process minimized sample dilution which remained constant as pool sizes increased. Compressed sensing can be adapted to a wide variety of diagnostic testing applications to increase throughput for routine laboratory testing as well as a means to increase testing capacity to combat future pandemics.Author summary: The rapid spread of COVID-19 highlighted the need for testing approaches that are rapid and accurate while reducing the use of critical testing reagents when resources are scarce. One method to increase testing throughput and reduce material usage is to pool samples prior to testing. With this method a single negative result indicates all samples within the pool are negative. However, the efficiency gains from pooling samples decreases when the infection prevalence rate is high, as with the COVID-19 pandemic, where many pools will return positive test results. In this study, we present a novel mathematical approach to pooled testing based on compressed sensing theory allowing us to accurately identify infected samples within a pool at high prevalence rates. Experimentally, we validated our compressed sensing method in a coronavirus model system as well as with primary human COVID-19 nasal swab samples. Using our method, we were able to reduce the number of tests required by 69% while identifying infected samples with 100% accuracy. Our compressed sensing pooled testing method exhibited high accuracy and reproducibility and offered several advantages including the conservation of vital supplies and increased throughput that may facilitate a more rapid response to future pandemics.