Determination of Varying Group Sizes for Pooling Procedure

Xiong, Wenjun; Lu, Hanqing; Ding, Juan

doi:10.1155/2019/4381084

Cited by 3 publications

(3 citation statements)

References 46 publications

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“…However, for increasing infection rates, smaller pool sizes result in more cases identified per test, which means the initial pool size can be optimised, see e.g. Hanel and Thurner and Xiong et al [ 17 , 23 ]. We optimise the pool size for each testing strategy and infection rate using a test population of 50, 000 individuals using every pool size in {1, …, 32}.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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“…Ref. 6). Unfortunately, such algorithms do not always fit the workflow of pathology and laboratory medicine.…”

Section: Introductionmentioning

confidence: 99%

Online algorithm for assignment of specimens to pooled or individual testing using risk models provides a practical way to increase testing capacity

Alekseyenko

2023

F1000Res

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Background: To improve throughput in diagnostic and screening testing for infectious diseases, I developed a straight-forward algorithm that uses individual risk to optimize the decision about pooled or individual testing. Methods: The online greedy algorithm provides an recommendation for filling pooled testing queue for optimal testing in pools of variable size. Observational data from Medical University of South Carolina COVID-19 diagnostic testing was used to estimate capacity gains under this algorithm versus optimal fixed pooling based on population prevalence. Results: The online pooling recommendations based on this algorithm resulted in statistically better capacity gains than optimal pools of fixed size (P-value 0.003 and 0.002, for pools of 5 or 6, respectively). This is especially significant since the underlying individual-level risk prediction model attained only a moderate predictive accuracy. Conclusions: This result suggests that when combined with a better risk prediction and integrated in an appropriate informatics ecosystem this approach cab offers an opportunity for resilient pooled testing strategies for pathogens while incorporating relevant operational constraints of pathology laboratories.

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“…First, sensitivity is reduced because of the dilution from negative samples, and second, selectivity is compromised due to a higher probability of cross-contamination . Moreover, optimizing the number of samples to be pooled together requires a prior estimation of the prevalence of the disease because if the size of the pool is too large and/or the prevalence of the disease is too high it will not reduce the overall number of tests as many pooled samples require retesting. Many studies have reported improvements in the sample pooling performance by using mathematical and statistical algorithms, for example, to predict the optimal pool size on the basis of the carrier rate; however, this still does not eliminate the need for the retest step.…”

mentioning

confidence: 99%

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

Zhuang

et al. 2022

Anal. Chem.

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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.

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Determination of Varying Group Sizes for Pooling Procedure

Cited by 3 publications

References 46 publications

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

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

Online algorithm for assignment of specimens to pooled or individual testing using risk models provides a practical way to increase testing capacity

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

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