Noisy Pooled PCR for Virus Testing

Abstract

Fast testing can help mitigate the coronavirus disease 2019 pandemic. Despite their accuracy for single sample analysis, infectious diseases diagnostic tools, like RT-PCR, require substantial resources to test large populations. We develop a scalable approach for determining the viral status of pooled patient samples. Our approach converts group testing to a linear inverse problem, where false positives and negatives are interpreted as generated by a noisy communication channel, and a message passing algorithm estimates the illness status of patients. Numerical results reveal that our approach estimates patient illness using fewer pooled measurements than existing noisy group testing algorithms. Our approach can easily be extended to various applications, including where false negatives must be minimized. Finally, in a Utopian world we would have collaborated with RT-PCR experts; it is difficult to form such connections during a pandemic. We welcome new collaborators to reach out and help improve this work! Index Terms-Approximate

Who Is Immune Against COVID-19 and Safe to Return to Work: The Impact of Laboratory Assays

“…Positive results are detected by the cleavage of the reporter on the probe and amplification of fluorescence enhancement. However, the accurate results of this technique may strongly depend on the onset time of symptoms and the specimens and routes of sampling (34,35). Furthermore, successive amplification of the viral genome in the suspected patient can be interpreted as a positive result when the cycle threshold value raises in > 40 cycles, otherwise, the result may be considered as negative (36).…”