Application-oriented mathematical algorithms for group testing

Csóka, Endre

doi:10.1101/2020.05.05.20091637

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Second-level pools can be built either by splitting positive pools in subpools (thus reducing the pool size at the second step) or rearranging samples completely (to use the same pool size in the two steps). Different strategies to build second-level subpools from positive first-level pools are discussed in [ 13 ]. Notice that, due to the structure of adaptative schemes, the different pooling steps have to be performed sequentially (in order to build the second pooling step, results of the first steps have to be known).…”

Section: Pooling Scheme: the Theoretical Point Of Viewmentioning

confidence: 99%

Test Groups, Not Individuals: A Review of the Pooling Approaches for SARS-CoV-2 Diagnosis

Millioni¹,

Mortarino

2021

Diagnostics

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Massive molecular testing for SARS-CoV-2 diagnosis is mandatory to manage the spread of COVID-19. Diagnostic screening should be performed at a mass scale, extended to the asymptomatic population, and repeated over time. An accurate diagnostic pipeline for SARS-CoV-2 that could massively increase the laboratory efficiency, while being sustainable in terms of time and costs, should be based on a pooling strategy. In the past few months, researchers from different disciplines had this same idea: test groups, not individuals. This critical review intends to highlight both the general consents—even if the results from different publications have been obtained with different protocols—and the points of disagreement that are creating some interpretative/comprehension difficulties. Different pooling schemes and technical aspects associated to the type of pooling adopted are described and discussed. We hope that this review can consolidate information to support researchers in designing optimized COVID-19 testing protocols based on pooling.

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Section: Pooling Scheme: the Theoretical Point Of Viewmentioning

confidence: 99%

Test Groups, Not Individuals: A Review of the Pooling Approaches for SARS-CoV-2 Diagnosis

Millioni¹,

Mortarino

2021

Diagnostics

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“…In fact, allowing for more than one round of testing, namely, adaptive testing, instead of one-shot recovery of results, can provide even more efficient outcomes. Especially for low prevalence regimes (i.e., P <1/K 2 ), N (2P + (1-2P)/K) measurements set a lower bound on the number of required tests, where P is the prevalence and K the limit of the pool size ( 34 ). This implies almost a couple of tests per a positive sample and a single test per pool—a very efficient scheme with large pools.…”

Section: Population Level Scanning For Covid-19mentioning

confidence: 99%

COVID-19 Pandemic: Group Testing

Nalbantoğlu

Gündoğdu

2020

Front. Med.

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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 [21][22][23][24][25] , combinatorial [26][27][28][29][30][31] , as well as information and coding theoretic [32][33][34][35][36][37][38][39][40][41][42][43][44][45] perspectives, as well as software 46;47 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 [48][49][50][51][52][53][54][55][56][57][58][59] , often in the low prevalence regime.…”

Section: Introductionmentioning

confidence: 99%

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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Application-oriented mathematical algorithms for group testing

Cited by 5 publications

References 5 publications

Test Groups, Not Individuals: A Review of the Pooling Approaches for SARS-CoV-2 Diagnosis

Test Groups, Not Individuals: A Review of the Pooling Approaches for SARS-CoV-2 Diagnosis

COVID-19 Pandemic: Group Testing

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

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