| Summary: | One of the main challenges in containing the Coronoavirus disease 2019
(COVID-19) pandemic stems from the difficulty in carrying out efficient mass
diagnosis over large populations. The leading method to test for COVID-19
infection utilizes qualitative polymerase chain reaction, implemented using
dedicated machinery which can simultaneously process a limited amount of
samples. A candidate method to increase the test throughput is to examine
pooled samples comprised of a mixture of samples from different patients. In
this work we study pooling-based COVID-19 tests. We identify the specific
requirements of COVID-19 testing, including the need to characterize the
infection level and to operate in a one-shot fashion, which limit the
application of traditional group-testing (GT) methods. We then propose a
multi-level GT scheme, designed specifically to meet the unique requirements of
COVID-19 tests, while exploiting the strength of GT theory to enable accurate
recovery using much fewer tests than patients. Our numerical results
demonstrate that multi-level GT reliably and efficiently detects the infection
levels, while achieving improved accuracy over previously proposed one-shot
COVID-19 pooled-testing methods.
|
|---|