Determination of Varying Group Sizes for Pooling Procedure

Unique Barcoded Primer-Assisted Sample-Specific Pooled Testing (Uni-Pool) for Large-Scale Screening of Viral Pathogens

Pooled testing has been widely adopted recently to facilitate large-scale community testing during the COVID-19 pandemic. This strategy allows to collect and screen multiple specimen samples in a single test, thus immensely saving the assay time and consumable expenses. Nevertheless, when the outcome of a pooled testing is positive, it necessitates repetitive retesting steps for each sample which can pose a serious challenge during a rising infection wave of increasing prevalence. In this work, we develop a unique barcoded primer-assisted sample-specific pooled testing strategy (Uni-Pool) where the key genetic sequences of the viral pathogen in a crude sample are extracted and amplified with concurrent tagging of sample-specific identifiers. This new process improves the existing pooled testing by eliminating the need for retesting and allowing the test results-positive or negative-for all samples in the pool to be revealed by multiplex melting curve analysis right after real-time polymerase chain reaction. It significantly reduces the total assay time for large-scale screening without compromising the specificity and detection sensitivity caused by the sample dilution of pooling. Our method was able to successfully differentiate five samples, positive and negative, in one pool with negligible cross-reactivity among the positive and negative samples. A pooling of 40 simulated samples containing severe acute respiratory syndrome coronavirus-2 pseudovirus of different loads (min: 10 copies/μL; max: 10³ copies/μL) spiked into artificial saliva was demonstrated in eight randomized pools. The outcome of five samples in one pool with a hypothetical infection prevalence of 15% in 40 samples was successfully tested and validated by a typical Dorman-based pooling.

Evaluation of pool-based testing approaches to enable population-wide screening for COVID-19

Objective

Rapid testing is paramount during a pandemic to prevent continued viral spread and excess morbidity and mortality. This study investigates whether testing strategies based on sample pooling can increase the speed and throughput of screening for SARS-CoV-2, especially in resource-limited settings.

Methods

In a mathematical modelling approach conducted in May 2020, six different testing strategies were simulated based on key input parameters such as infection rate, test characteristics, population size, and testing capacity. The situations in five countries were simulated, reflecting a broad variety of population sizes and testing capacities. The primary study outcome measurements were time and number of tests required, number of cases identified, and number of false positives.

Findings

The performance of all tested methods depends on the input parameters, i.e. the specific circumstances of a screening campaign. To screen one tenth of each country’s population at an infection rate of 1%, realistic optimised testing strategies enable such a campaign to be completed in ca. 29 days in the US, 71 in the UK, 25 in Singapore, 17 in Italy, and 10 in Germany. This is ca. eight times faster compared to individual testing. When infection rates are lower, or when employing an optimal, yet more complex pooling method, the gains are more pronounced. Pool-based approaches also reduce the number of false positive diagnoses by a factor of up to 100.

Conclusions

The results of this study provide a rationale for adoption of pool-based testing strategies to increase speed and throughput of testing for SARS-CoV-2, hence saving time and resources compared with individual testing.