Error Correction Codes for COVID-19 Virus and Antibody Testing: Using Pooled Testing to Increase Test Reliability

Abstract

We consider a novel method to increase the reliability of COVID-19 virus or antibody tests by using specially designed pooled testings. Specifically, to increase test reliability, instead of testing nasal swab or blood samples from individual persons, we propose to test mixtures of samples from many individuals. Group testing has traditionally been used for the purpose of reducing the number of tests required to diagnose a large number of individuals, but, in contrast, the pooled sample testing method proposed in this paper also serves a different purpose: for increasing test reliability and providing accurate diagnoses even if the tests themselves are not very accurate. Our method uses ideas from compressed sensing and errorcorrection coding to correct for a certain number of errors in the test results. The intuition is that when each individual's sample is part of many pooled sample mixtures, the test results from all of the sample mixtures contain redundant information about each individual's diagnosis, which can be exploited to automatically correct for wrong test results in exactly the same way that error correction codes correct errors introduced in noisy communication channels. While such redundancy can also be achieved by simply testing each individual's sample multiple times, we present simulations and theoretical arguments that show that our method is significantly more efficient in increasing diagnostic accuracy. In contrast to group testing and compressed sensing which aim to reduce the number of required tests, this proposed error correction code idea purposefully uses pooled testing to increase test accuracy, and works not only in the "undersampling" regime, but also in the "oversampling" regime, where the number of tests is bigger than the number of subjects. The results in this paper run against traditional beliefs that, "even though pooled testing increased test capacity, pooled testings were less reliable than testing individuals separately."

High capacity clinical SARS-CoV-2 molecular testing using combinatorial pooling

“…The clinical testing described here utilized the P-BEST (Pooling-Based Efficient SARS-CoV-2 Testing) method, which, as opposed to former methods, is a non-adaptive group testing approach that requires only a single round of testing 5 . Several other papers have since presented analogous non-adaptive approaches [12][13][14][15][16] , yet, to the best of our knowledge, none of these methods were utilized in clinical settings.…”