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

Cassidy Mentus; Martin Romeo; Christian DiPaola

We nd that DT outperforms GBS on average even though GBS in theory uses close to optimal number of tests for a known xed number of (+) cases (bounded above by #tests − 1 + information lower bound). This indicates that methods based on DT have the potential to save many tests. We found that DT when applied without a capped recovers close to the optimal 1 bit of information with each group-test. If the measurement is sensitive to detect one positive in groups of size twice up to 16 as large as the current maximum, then optimally ecient testing can be carried out. It might be the case that a full binary search of the group puts too much strain on the laboratory work ow. The bit of information that gained from negative test results on large pools can be leveraged while choosing smaller sample sub-groups to carry out the process of nding positive cases.