RT Journal Article
SR Electronic
T1 Improving COVID-19 Testing Efficiency using Guided Agglomerative Sampling
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.13.039792
DO 10.1101/2020.04.13.039792
A1 Fayyaz Minhas
A1 Dimitris Grammatopoulos
A1 Lawrence Young
A1 Imran Amin
A1 David Snead
A1 Neil Anderson
A1 Asa Ben-Hur
A1 Nasir Rajpoot
YR 2020
UL http://biorxiv.org/content/early/2020/04/14/2020.04.13.039792.abstract
AB One of the challenges in the current COVID-19 crisis is the time and cost of performing tests especially for large-scale population surveillance. Since, the probability of testing positive in large population studies is expected to be small (<15%), therefore, most of the test outcomes will be negative. Here, we propose the use of agglomerative sampling which can prune out multiple negative cases in a single test by intelligently combining samples from different individuals. The proposed scheme builds on the assumption that samples from the population may not be independent of each other. Our simulation results show that the proposed sampling strategy can significantly increase testing capacity under resource constraints: on average, a saving of ~40% tests can be expected assuming a positive test probability of 10% across the given samples. The proposed scheme can also be used in conjunction with heuristic or Machine Learning guided clustering for improving the efficiency of large-scale testing further. The code for generating the simulation results for this work is available here: https://github.com/foxtrotmike/AS.Competing Interest StatementThe authors have declared no competing interest.