Analysis and Applications of Adaptive Group Testing Methods for COVID-19

Mentus, Cassidy; Romeo, Martin; DiPaola, Christian

Abstract Testing strategies for Covid-19 to maximize number of people tested is urgently needed. Recently, it has been demonstrated that RT-PCR has the sensitivity to detect one positive case in a mixed sample 32 cases [9]. In this paper we propose non-adaptive and adaptive group testing strategies based on generalized binary splitting (GBS) [2] where we restrict the group test to the largest group that can be used. The method starts by choosing a group from the population to be tested, performing a test on the combined sample from the entire group and progressively splitting the group further into subgroups. Compared to individual testing at 4% prevalence we save 74% at 1% we save 91% and at 1% we save 97% of tests. We analyze the number of times each sample is used and show the method is still efficient if we resort to testing a case individually if the sample is running low. Abstract In addition we recommend clinical screening to filter out individuals with symptoms and show this leaves us with a population with lower prevalence. Our approach is particularly applicable to vulnerable confined populations such as nursing homes, prisons, military ships and cruise ships.