Discussion on “Is group testing ready for prime‐time in disease identification”

Bilder, Christopher R.; Tebbs, Joshua M.; McMahan, Christopher S.

doi:10.1002/sim.8988

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…Thus, there can be less pathogen present for the test performed upon the group than for a test performed upon a single specimen. Bilder et al (2021b) discussed the potential dilution effect and solutions for it. In summary, the resulting outcome from the dilution effect is often known for assays (Tan et al 2020), like for those using real-time reverse transcription polymerase chain reaction (RT-qPCR) which was widely used for SARS-CoV-2 testing.…”

Section: Identificationmentioning

confidence: 99%

binGroup2: Statistical Tools for Infection Identification via Group Testing

R. Bilder,

D. Hitt,

J. Biggerstaff

et al. 2024

The R Journal

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Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm's operating characteristics, such as the expected number of tests. Our paper presents the binGroup2 package that provides the statistical tools for this purpose. This R package is the first to address the identification aspect of group testing for a wide variety of algorithms. We illustrate its use through COVID-19 and chlamydia/gonorrhea applications of group testing.

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Section: Identificationmentioning

confidence: 99%

binGroup2: Statistical Tools for Infection Identification via Group Testing

R. Bilder,

D. Hitt,

J. Biggerstaff

et al. 2024

The R Journal

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“…Due to the critical importance of testing programmes, there is a large body of literature exploring different testing schemes. The potential for the use of pooled testing (defined below) in epidemics has been suggested since its development in 1943 with increased interest during the recent COVID‐19 pandemic including Statistics in Medicine hosting a discussion on whether pooled testing was ready for use in the pandemic 6‐14 . Pooled testing combines samples from multiple individuals into a single sample that can then tested together.…”

Section: Introductionmentioning

confidence: 99%

“…The potential for the use of pooled testing (defined below) in epidemics has been suggested since its development in 1943 with increased interest during the recent COVID-19 pandemic including Statistics in Medicine hosting a discussion on whether pooled testing was ready for use in the pandemic. [6][7][8][9][10][11][12][13][14] Pooled testing combines samples from multiple individuals into a single sample that can then tested together. This means pooled testing can test the same number of individuals at a fraction of the cost or, equivalently, using less resources.…”

mentioning

confidence: 99%

How could a pooled testing policy have performed in managing the early stages of the COVID‐19 pandemic? Results from a simulation study

Heath,

Villar,

Robertson

2024

Statistics in Medicine

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A coordinated testing policy is an essential tool for responding to emerging epidemics, as was seen with COVID‐19. However, it is very difficult to agree on the best policy when there are multiple conflicting objectives. A key objective is minimizing cost, which is why pooled testing (a method that involves pooling samples taken from multiple individuals and analyzing this with a single diagnostic test) has been suggested. In this article, we present results from an extensive and realistic simulation study comparing testing policies based on individually testing subjects with symptoms (a policy resembling the UK strategy at the start of the COVID‐19 pandemic), individually testing subjects at random or pools of subjects randomly combined and tested. To compare these testing methods, a dynamic model compromised of a relationship network and an extended SEIR model is used. In contrast to most existing literature, testing capacity is considered as fixed and limited rather than unbounded. This article then explores the impact of the proportion of symptomatic infections on the expected performance of testing policies. Symptomatic testing performs better than pooled testing unless a low proportion of infections are symptomatic. Additionally, we include the novel feature for testing of non‐compliance and perform a sensitivity analysis for different compliance assumptions. Our results suggest for the pooled testing scheme to be superior to testing symptomatic people individually, only a small proportion of the population () needs to not comply with the testing procedure.

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“…In 2020, pooled testing was identified as an important strategy during the COVID-19 pandemic 9 – 14 . Many countries implemented pooled testing for detecting the SARS-CoV-2 virus with polymerase chain reaction (PCR)-based assays, with varying pool size depending on disease prevalence 15 – 18 . From these reports, pooled testing with various pooling strategies enabled estimated reductions of 40–90% in the number of assays needed as compared to individual testing, with only minor decreases in viral detection.…”

Section: Introductionmentioning

confidence: 99%

Implementation of large-scale pooled testing to increase rapid molecular diagnostic test coverage for tuberculosis: a retrospective evaluation

Vuchas,

Teyim,

Dang

et al. 2023

Sci Rep

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In 2021, only 6.4 million of the 10.6 million people with tuberculosis (TB) were diagnosed and treated for the disease. Although the World Health Organization recommends initial diagnostic testing using a rapid sensitive molecular assay, only 38% of people diagnosed with TB benefited from these, due to barriers including the high cost of available assays. Pooled testing has been used as an approach to increase testing efficiency in many resource-constrained situations, such as the COVID-19 pandemic, but it has not yet been widely adopted for TB diagnostic testing. Here we report a retrospective analysis of routine pooled testing of 10,117 sputum specimens using the Xpert MTB/RIF and Xpert MTB/RIF Ultra assays that was performed from July 2020 to February 2022. Pooled testing saved 48% of assays and enabled rapid molecular testing for 4156 additional people as compared to individual testing, with 6.6% of specimens positive for TB. From an in silico analysis, the positive percent agreement of pooled testing in pools of 3 as compared with individual testing for the Xpert MTB/RIF Ultra assay was estimated as 99.4% (95% CI, 96.6% to 100%). These results support the scale-up of pooled testing for efficient TB diagnosis.

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Discussion on “Is group testing ready for prime‐time in disease identification”

Cited by 5 publications

References 27 publications

binGroup2: Statistical Tools for Infection Identification via Group Testing

binGroup2: Statistical Tools for Infection Identification via Group Testing

How could a pooled testing policy have performed in managing the early stages of the COVID‐19 pandemic? Results from a simulation study

Implementation of large-scale pooled testing to increase rapid molecular diagnostic test coverage for tuberculosis: a retrospective evaluation

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