COVID-19 Pandemic: Group Testing

Nalbantoğlu, Ufuk; Gündoğdu, Aycan

doi:10.3389/fmed.2020.00522

Cited by 3 publications

(3 citation statements)

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“…There has been tremendous study and progress on pooled testing (also called group testing or specimen pooling) in general. Numerous works provide statistical 20–24 , combinatorial 25–29 , and information theoretic 2;30–38 perspectives, as well as software 39;40 to aid implementation, to name just a few. Additionally, there has been a lot of work on analyzing and optimizing group testing for various constraints and evaluation criteria 41–51 , often in the low prevalence regime.…”

Section: Introductionmentioning

confidence: 99%

“…As such, widespread, scalable and frequent testing is a defining challenge in combatting COVID-19 in the face of local, national and global resource constraints. Pooled testing has recently arisen as a promising efficient scientific solution to the world-wide challenge of increasing SARS-CoV-2 testing capacity [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] , encouraged in part by the finding that a single positive sample can be reliably detected by RT-qPCR in large pools 18 .…”

Section: Introductionmentioning

confidence: 99%

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HYPER: Group testing via hypergraph factorization applied to COVID-19

Hong

Dey

Lin

et al. 2021

Preprint

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Large scale screening is a critical tool in the life sciences, but is often limited by reagents, samples, or cost. An important challenge in screening has recently manifested in the ongoing effort to achieve widespread testing for individuals with SARS-CoV-2 infection in the face of substantial resource constraints. Group testing methods utilize constrained testing resources more efficiently by pooling specimens together, potentially allowing larger populations to be screened with fewer tests. A key challenge in group testing is to design an effective pooling strategy. The global nature of the ongoing pandemic calls for something simple (to aid implementation) and flexible (to tailor for settings with differing needs) that remains efficient. Here we propose HYPER, a new group testing method based on hypergraph factorizations. We provide characterizations under a general theoretical model, and exhaustively evaluate HYPER and proposed alternatives for SARS-CoV-2 screening under realistic simulations of epidemic spread and within-host viral kinetics. We demonstrate that HYPER performs at least as well as other methods in scenarios that are well-suited to each method, while outperforming those methods across a broad range of resource-constrained environments, and being more flexible and simple in design, and taking no expertise to implement. An online tool to implement these designs in the lab is available at http://hyper.covid19-analysis.org.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HYPER: Group testing via hypergraph factorization applied to COVID-19

Hong

Dey

Lin

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Pooled testing can be used to significantly cut down the equipment costs and make the tests cheaper; it can also facilitate increased numbers of testing. There has been a global call to use pooled testing to ramp up the efforts in testing (see, e.g., Bilder et al, 2020;Broadfoot, 2020;Halbfinger, 2020;Hanel & Thurner, 2020;Lakdawalla et al, 2020;Nalbantoglu and Gundogdu, 2020;Pouwels et al, 2020). The demand has found resonance in India, see Biswas (2020), Kattakayam (2020), Seethalakshmi and Rahul (2020), and Mallapaty, (2020) and the Indian Council for Medical Research (ICMR) had given its approval (see ICMR, 2020aICMR, , 2020b and issued detailed guidelines.…”

Section: Introductionmentioning

confidence: 99%

Application of Pooled Testing Methodologies in Tackling the COVID-19 Pandemic

Guha

2021

Management and Labour Studies

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The recent emergence of the COVID-19 pandemic has posed a healthcare challenge, as well as an economic threat to the world. The situation is even worse for the developing nations like India. Diagnostic tests are fundamental to the ability to detect and respond to the disease. However, finding adequate resources for that has proved to be a significant challenge. As a solution, a pooling approach for the diagnostic tests is proposed in the literature and the media. In this article, we explore several algorithms for pooling samples to reduce the number of such diagnostic tests. Through a comparative study, we look at their relative performance at different disease prevalence levels. We also discuss the impact of the sensitivity and specificity of the tests. In the context of using the quantitative reverse-transcription polymerase chain reaction (RT-qPCR) tests for diagnosing COVID-19 cases in India, we discuss the feasibility of using the pooling algorithms and possible avenues to improve their effectiveness.

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Group testing via hypergraph factorization applied to COVID-19

et al. 2022

View full text Add to dashboard Cite

Large scale screening is a critical tool in the life sciences, but is often limited by reagents, samples, or cost. An important recent example is the challenge of achieving widespread COVID-19 testing in the face of substantial resource constraints. To tackle this challenge, screening methods must efficiently use testing resources. However, given the global nature of the pandemic, they must also be simple (to aid implementation) and flexible (to be tailored for each setting). Here we propose HYPER, a group testing method based on hypergraph factorization. We provide theoretical characterizations under a general statistical model, and carefully evaluate HYPER with alternatives proposed for COVID-19 under realistic simulations of epidemic spread and viral kinetics. We find that HYPER matches or outperforms the alternatives across a broad range of testing-constrained environments, while also being simpler and more flexible. We provide an online tool to aid lab implementation: http://hyper.covid19-analysis.org.

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COVID-19 Pandemic: Group Testing

Cited by 3 publications

References 35 publications

HYPER: Group testing via hypergraph factorization applied to COVID-19

HYPER: Group testing via hypergraph factorization applied to COVID-19

Application of Pooled Testing Methodologies in Tackling the COVID-19 Pandemic

Group testing via hypergraph factorization applied to COVID-19

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