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

Abstract

Testing strategies for Covid-19 to maximize number of people tested are urgently needed. Recently, it has been demonstrated that RT-PCR has the sensitivity to detect one positive case in a mixed sample of 32 cases [12], In this paper we propose adaptive group testing strategies based on generalized binary splitting (CBS) [5], 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 98% of tests. We analyze the number of times each sample is used and show that the method is still efficient if we resort to testing a case individually if the sample is running low.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.