Author: Cassidy Mentus; Martin Romeo; Christian DiPaola
Title: Analysis and Applications of Non-Adaptive and Adaptive Group Testing Methods for COVID-19 Document date: 2020_4_7
ID: 3sr4djft_111
Snippet: We test the performance of two algorithms: divide and test and generalized binary splitting. For populations of size 100 and 1000 with each case being positive independent of one another with probability p, we nd that both methods save many tests. In fact, even when we restrict the size of the groups they make substantial savings. For example at prevalence .001 and group sizes capped at 16, both DT16 and GBS16 are capable of outperforming Dorfman.....
Document: We test the performance of two algorithms: divide and test and generalized binary splitting. For populations of size 100 and 1000 with each case being positive independent of one another with probability p, we nd that both methods save many tests. In fact, even when we restrict the size of the groups they make substantial savings. For example at prevalence .001 and group sizes capped at 16, both DT16 and GBS16 are capable of outperforming Dorfman's method, that requires a group size of 33 for optimal performance. For a population of 1000 and condence level .99, the DT16 and GBS16 also have a similar performance to Dorfman's method at .001 where Dorfman's method is chosen as if we knew the exact prevalence.
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