A reduction of the number of assays and turnaround time by optimizing polymerase chain reaction (PCR) pooled testing for SARS-CoV-2

Abstract

Early detection of the severe acute respiratory syndrome coronavirus 2 infection can decrease the spread of the disease and provide therapeutic options promptly in affected individuals. However, the diagnosis by reverse-transcription polymerase chain reaction is costly and time-consuming. Several methods of group testing have been developed to overcome this problem. The proposed strategy offers optimization of group testing according to the available resources by decreasing not only the number of the assays but also the turnaround time. The initial classification of the samples would be done according to the intention of testing defined as diagnostic or screening/surveillance, achieving the best possible homogeneity. The proposed stratification of pooling is based on branching (divisions) and depth (levels of re-pooling) of the original group in association with the estimated probability of a positive sample. The dilutional effect of the grouped samples has also been considered. The margins of minimum and maximum conservation of assays of pooled specimens are calculated and the optimum strategy can be selected in association with the probability of positive samples in the original group. This algorithm intends to be a useful tool for group testing offering a choice of strategies according to the requirements.

Keywords: COVID-19; RT-PCR; SARS-CoV-2; group testing; pooled analysis; sample pooling; sensitivity; turnaround time.

Figure 1

The proposed algorithm. Any symbol A + superscript means “the samples of A that test positive.” N, the population to be tested; b, the branching factor of the algorithm; PUC, pool under consideration; C _N,N+,b, the number of pools in PUC at step I; T(S), the expected number of replicates required to test a pool of size S; T ^# _N,b(N ⁺), the total number of tests required using our method to find the N ⁺ positives in a population of N; t _N,b(p), the expected ratio of tests performed with our proposed method and by testing each sample individually

Figure 2

Graphic illustration of the ratio of the number of tests using grouping (NTUG) over the number of tests without grouping (NTWG) (NTUG/NTWG) required to identify all the positive samples in different combinations of branching and depth. The vertical axis shows the ratio NTUG/NTWG. The horizontal axis shows the probability of positive samples (percentage of positivity). The blue line shows the maximum possible ratio NTUG/NTWG versus the percentage of positivity, which occurs in the case that the samples are distributed evenly among the groups (worst possible scenario). The orange line shows the same ratio obtained when the samples are distributed with the most favorable distribution (positive samples are concentrated leaving most groups free from positive samples‐best possible scenario). The gray line shows the graphic representation of the above parameters when each sample is tested individually (replacing NTUG by NTWG in the estimated ratio)

Figure 3

(A) Schematic demonstration of branching = 2 and depth = 4 in a group of eight samples and schematic demonstration of comparing two pooling methods with the maximum sample size of 16. (B) Pooling method of branching = 4, depth = 3 and (C) pooling method of branching = 2, depth = 5

Figure 4

Schematic demonstration of the two cases. A group of 16 samples with two samples positive (probability 12.5%) is analyzed with branching = 4 and depth = 3. Case A: grouping with the best possible distribution of positive samples, a total of 9 assays were performed, NTUG/NTWG = 9/16 (0.56). Case B: grouping the worst possible distribution of positive samples, a total of 13 assays were performed, NTUG/NTWG = 13/16 (0.81). NTUG/NTWG, The ratio of the number of tests using grouping (NTUG) over the number of tests without grouping (NTWG). Groups shown in red represent positive samples