A Nonadaptive Combinatorial Group Testing Strategy to Facilitate Health Care Worker Screening during the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Outbreak

Abstract

\n Routine testing for SARS-CoV-2 in healthcare workers (HCWs) has been suggested to prevent healthcare facilities becoming persistent reservoirs of infectivity. Group-testing strategies have been proposed to increase capacity, but these have been designed to facilitate mass population testing and do not prioritize turnaround time, an important consideration for HCW screening. We propose a non-adaptive combinatorial (NAC) group-testing strategy to increase throughput whilst facilitating rapid turnaround. NAC matrices were constructed for sample sizes of 700, 350 and 250. Matrix performance was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1-10%. NAC matrices were compared to Dorfman Sequential (DS) group-testing approaches. NAC matrices performed well at low prevalence levels with an average of 97% of samples resolved after a single round of testing via the n=700 matrix at a prevalence of 1%. In simulations of low to medium (0.1%-3%) prevalence all NAC matrices were superior to the DS strategy, measured by fewer repeated tests required. At very high prevalence levels (10%) the DS matrix was marginally superior, however both group-testing approaches performed poorly at high prevalence levels. This strategy maximises the proportion of samples resolved after a single round of testing, allowing prompt return of results to HCWs. Using this methodology, laboratories can adapt their testing scheme based on required throughput and the current population prevalence, facilitating a data-driven testing strategy.\n