Noisy Pooled PCR for Virus Testing
This addresses the need for fast and resource-efficient testing during a pandemic, though it appears incremental as it builds on existing noisy group testing algorithms.
The paper tackles the problem of scaling virus testing for COVID-19 by developing a noisy pooled PCR approach that converts group testing into a linear inverse problem, using a message passing algorithm to estimate patient illness with fewer pooled measurements than existing methods, as shown in numerical results.
Fast testing can help mitigate the coronavirus disease 2019 (COVID-19) 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!