Evaluating The Efficiency Of Sample Pooling For Real-Time PCR Based Diagnosis Of COVID-19


The novel coronavirus, COVID-19, was first detected in Wuhan, China in December, 2019. This rapidly emerged as a worldwide threat to public health and spread globally in a couple of months. Severe acute respiratory syndrome coronavirus 2, the pathogen of coronavirus pandemic has shed light on the need for early diagnosis of the quickly spreading infectious diseases using various biology products. A rapid diagnosis provides for better containment and eventually, control. Diagnosing COVID-19 rapidly is essential for evaluating the spread of disease and to trace the comtacts of those individuals who caught the virus.

There is plenty proof that countries able to screen out patients gradually have fared comparatively better in containing the virus and reduce the mortality rate due to the disease. Lab arrangements for testing the severe acute respiratory syndrome has been im-peded due to the considerable strain on global supply chains for equipment reagents, ppe, and other consumables.

Most countries have experienced an acute shortage of essential reagents required for the PCR assay for SARS-CoV 2. It is easily understood that rapid diagnosis of COVID 19 in both asymptomatic and symptomatic patients can elaborate regarding the transmission patterns and also be able to facilitate contact tracing to adopt a strategy for the containment. Screening for COVID 19 on a large scale population is generally considered an important part that has been advocated by ICMR and WHO.

Materials And Methodology

The pooling strategy followed here is a simple two-stage testing algorithm called Dorfman pooling. In the first stage, the samples are divided into disjoint pools then each pool is subjected to testing. A negative result suggests that all samples in the pool are negative whereas a positive result suggests that at least one of the samples in the pool is positive.

In the second stage, each pool’s samples that tested positive are separately tested. For assessing the group test strategy, initial step was to calculate the most effective pool size. Epidemiological studies are under the pipeline to determine how to prevent COVID-19 is as it has been found to spread rapidly throughout the world at a high-speed rate. However, the preliminary evaluation over a short span at the labs has observed a positive sample rate within the tested community. In 5-7 weeks, four positive samples detected at different levels of CT values for both E-gene and RdRp were evaluated in the mentioned dilutions. This enabled an objective assessment of dilutions and Ct values of the pool sample study. The trends in variations of CT values were discovered in the mentioned dilutions. The positive samples after these evaluations were re-evaluated in different ratios. So, the average change in Ct values was +2.56 for pools of 5 and +3.38 for a pool of 10, which was used in fixing the most suited cut-off CT value of the pooled sample. The cut-off was raised accordingly for a pool of samples of 5 and 10 by +3 and +4, respectively. Therefore, a CT value of less than 39 for a sample pool of 5 and less than 40 for a pool of 10 was understood and utilized in the study.

Samples that emerged negative in the pool testing were analyzed as negative and positive ones in the pool were retested as deconvoluted samples before the report was finalized. The cost of consumables and kits was also calculated in the tested pool samples, and the samples were deconvoluted to estimate the utilization of resources. The generated data were subjected to analysis using the help of appropriate statistical tools.


The study test results displayed that among 158 pools of 10 samples, the data analyzed that 11 pools were tested positive. Among 3503 pools of 5 samples, 164 pools tested positive. Out of 11 positive pools of 10 samples, 110 deconvoluted samples were tested, and ten individual samples were positive for COVID-19.  In a pool of 10, with Ct 38.24 and 39.89 for E gene and RdRp gene. There was no positive sample obtained after deconvolution. Out of the 5 sample pools, 164 pools were tested positive that comprised 820 samples. Among the deconvoluted 820 samples, 171 samples tested positive. In the above study and research process, 181 samples were tested positive out of 19095 samples with the positivity percentage of 0.95%.

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