Group Testing for Severe Acute Respiratory Syndrome- Coronavirus 2 to Enable Rapid Scale-up of Testing and Real-Time Surveillance of Incidence

Abstract

High-throughput molecular testing for severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) may be enabled by group testing in which pools of specimens are screened, and individual specimens tested only after a pool tests positive. Several laboratories have recently published examples of pooling strategies applied to SARS-CoV-2 specimens, but overall guidance on efficient pooling strategies is lacking. Therefore we developed a model of the efficiency and accuracy of specimen pooling algorithms based on available data on SAR-CoV-2 viral dynamics. For a fixed number of tests, we estimate that programs using group testing could screen 2-20 times as many specimens compared with individual testing, increase the total number of true positive infections identified, and improve the positive predictive value of results. We compare outcomes that may be expected in different testing situations and provide general recommendations for group testing implementation. A free, publicly-available Web calculator is provided to help inform laboratory decisions on SARS-CoV-2 pooling algorithms.

Keywords: COVID-19; SARS-CoV-2; diagnostic testing; group testing; pooled testing; screening; surveillance.

Figure 1.

Model for nasopharyngeal (NP) RNA in acute severe acute respiratory syndrome–coronavirus 2 (SARS CoV-2) infection. The viral dynamic model shown here reflects viral load dynamics for an average acutely infected individual from the first to the last day with RNA levels above the limit of detection (of a standard assay, used on an individual specimen). Model parameters were selected to reflect typical (noncritical) infection. All viral load information is shown on a log scale. A, Model parameters, with a 14-day window of detection, 1 log₁₀/day up and down slope on either side of a 6-day viral load plateau. B, Effect of specimen pooling on the window of detection and how sensitivity is estimated based on changes in the detection window. The calculation used to determine the maximum allowable pool size of 25 (see Results) is shown. C, Distribution of viral loads predicted by the model compared with those reported for testing populations in Hong Kong [7] and Nebraska [3]. The model-predicted distribution was estimated assuming that individuals with noncritical SARS CoV-2 infection would arrive for testing at uniform times during the detection window. For the Hong Kong study, results are those reported for individuals who were NP RNA positive at their first test. The Nebraska group reported the distribution for all positive testers. Boxes illustrate medians and interquartile ranges (IQRs); to allow comparison across studies, we estimated the IQR for the Nebraska study based on mean (standard deviation) of 27 (5.8) and an assumption of normality.

Figure 2.

Expected number of patients tested and coronavirus disease 2019 (COVID-19) cases identified by individual versus group testing using a fixed number of molecular tests. Expected results are estimated for laboratories according to the prevalence of detectable severe acute respiratory syndrome–coronavirus 2 (SARS CoV-2) RNA in tested samples. Scenarios span situations with low prevalence (0.001; eg, surveillance [5]) and high prevalence (0.10; eg, clinical testing), and expected results are compared for individual testing, 2-stage group testing, and 3-stage group testing. A, COVID-19 cases identified, shown as the expected number of COVID-19 cases (ie, true-positive samples) found per 1000 assays used. B, Individuals tested, shown as the estimated number of samples with finalized results per 1000 assays, for individual and group testing.

Figure 3.

Expected differences in efficiency for different pooling strategies with master pool sizes of 5, 15, or 25, at various levels of prevalence. Testing efficiency as a function of group testing strategy and severe acute respiratory syndrome–coronavirus 2 (SARS CoV-2) prevalence. Model estimates for testing efficiency are shown for 5 testing strategies at levels of prevalence from 0.001 to 0.50. The strategies shown are individual testing, 5:1 minipools, 15:1 minipools, 15:5:1 (3-stage) pools, 25:1 minipools, and 25:5:1 (3-stage) pools. Prevalence was defined as the proportion of specimens with RNA levels above the assay cutoff. Efficiency was defined as the number of all results obtained (positive and negative) divided by the number of tests performed and expressed as results per test; individual testing has an efficiency of 1.