tetano
Editor, Senior Moderator
Infect Chemother
. 2021 Mar;53(1):118-127.
doi: 10.3947/ic.2021.0005.
Experimental and Mathematical Optimization of a Pooling Test for Detection of SARS-CoV-2 in a Population with Low Viral Load
Hyeongseok Jeong[SUP] 1 [/SUP], Jooyeon Lee[SUP] 1 [/SUP], Shinhye Cheon[SUP] 1 [/SUP], Kyung Mok Sohn[SUP] 1 [/SUP], Jungok Kim[SUP] 1 [/SUP], Sungmin Kym[SUP] 1 [/SUP], Yeon Sook Kim[SUP] 2 [/SUP]
Affiliations
Abstract
Background: A pooling test is a useful tool for mass screening of coronavirus disease 2019 (COVID-19) in the pandemic era. We aimed to optimize a simple two-step pooling test by estimating the optimal pool size using experimental and mathematical validation.
Materials and methods: Experimental pools were created by mixing one positive respiratory sample with various numbers of negative samples. We selected positive samples with cycle threshold (Ct) values greater than 32 to validate the efficiency of the pooling test assuming a high likelihood of false-negative results due to low viral loads. The positivities of the experimental pools were investigated with a single reverse-transcription polymerase chain reaction (RT-PCR) using the U-TOP™ COVID-19 Detection Kit Plus (Seasun Biomaterials, Daejeon, Korea). We used the Dorfman equation to calculate the optimal size of a pooling test mathematically.
Results: Viral RNA could be detected in a pool with a size up to 11, even if the Ct value of a positive sample was about 35. The Dorfman equation showed that the optimal number of samples in a pool was 11 when the prevalence was assumed to be 0.66% based on the test positivity in Daejeon, Korea from April 1, 2020 to November 10, 2020. The efficiency of the pooling test was 6.2, which can save 83.9 of 100 individual tests.
Conclusion: Eleven samples in a pool were validated optimal experimentally assuming a prevalence of 0.66%. The pool size needs modification as the pandemic progresses; thus, the prevalence should be carefully estimated before pooling tests are conducted.
Keywords: Optimization; Pool size; Pooling test; SARS-CoV-2.
. 2021 Mar;53(1):118-127.
doi: 10.3947/ic.2021.0005.
Experimental and Mathematical Optimization of a Pooling Test for Detection of SARS-CoV-2 in a Population with Low Viral Load
Hyeongseok Jeong[SUP] 1 [/SUP], Jooyeon Lee[SUP] 1 [/SUP], Shinhye Cheon[SUP] 1 [/SUP], Kyung Mok Sohn[SUP] 1 [/SUP], Jungok Kim[SUP] 1 [/SUP], Sungmin Kym[SUP] 1 [/SUP], Yeon Sook Kim[SUP] 2 [/SUP]
Affiliations
- PMID: 34409785
- DOI: 10.3947/ic.2021.0005
Abstract
Background: A pooling test is a useful tool for mass screening of coronavirus disease 2019 (COVID-19) in the pandemic era. We aimed to optimize a simple two-step pooling test by estimating the optimal pool size using experimental and mathematical validation.
Materials and methods: Experimental pools were created by mixing one positive respiratory sample with various numbers of negative samples. We selected positive samples with cycle threshold (Ct) values greater than 32 to validate the efficiency of the pooling test assuming a high likelihood of false-negative results due to low viral loads. The positivities of the experimental pools were investigated with a single reverse-transcription polymerase chain reaction (RT-PCR) using the U-TOP™ COVID-19 Detection Kit Plus (Seasun Biomaterials, Daejeon, Korea). We used the Dorfman equation to calculate the optimal size of a pooling test mathematically.
Results: Viral RNA could be detected in a pool with a size up to 11, even if the Ct value of a positive sample was about 35. The Dorfman equation showed that the optimal number of samples in a pool was 11 when the prevalence was assumed to be 0.66% based on the test positivity in Daejeon, Korea from April 1, 2020 to November 10, 2020. The efficiency of the pooling test was 6.2, which can save 83.9 of 100 individual tests.
Conclusion: Eleven samples in a pool were validated optimal experimentally assuming a prevalence of 0.66%. The pool size needs modification as the pandemic progresses; thus, the prevalence should be carefully estimated before pooling tests are conducted.
Keywords: Optimization; Pool size; Pooling test; SARS-CoV-2.