A greedy adaptive algorithm identifies the infected subset in O(H(X)+mu) expected tests for any correlated infection distribution over a hypergraph, with extensions to semi-non-adaptive and noisy settings.
On Accelerated Testing for COVID-19 Using Group Testing
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abstract
COVID-19 has resulted in a global health crisis that may become even more acute over the upcoming months. One of the main reasons behind the current rapid growth of COVID-19 in the U.S. population is the limited availability of testing kits and the relatively-high cost of screening tests. In this draft, we demonstrate the effectiveness of group testing (pooling) ideas to accelerate testing for COVID-19. This draft is semi-tutorial in nature and is written for a broad audience with interest in mathematical formulations relevant to COVID-19 testing. Therefore, ideas are presented through illustrative examples rather than through purely theoretical formulations. The focus is also on pools of size less than 64 such as what is practical with current RT-PCR technology.
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Group Testing with General Correlation Using Hypergraphs
A greedy adaptive algorithm identifies the infected subset in O(H(X)+mu) expected tests for any correlated infection distribution over a hypergraph, with extensions to semi-non-adaptive and noisy settings.