scholarly journals Erratum: Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies

2021 ◽  
Vol 9 ◽  
Author(s):  
Frontiers Production Office
Keyword(s):  
Group Testing ◽  
Testing Strategies ◽  
Efficiency Increase

scholarly journals Group testing for SARS-CoV-2 allows for up to 10-fold efficiency increase across realistic scenarios and testing strategies

2020 ◽  
Author(s):  
Claudio M. Verdun ◽  
Tim Fuchs ◽  
Pavol Harar ◽  
Dennis Elbrächter ◽  
David S. Fischer ◽  
...  
Keyword(s):  
Infection Rate ◽  
Web Application ◽  
Group Testing ◽  
Cross Sectional ◽  
Testing Procedures ◽  
Fine Grained ◽  
Testing Strategies ◽  
Care Workers ◽  
Efficiency Increase ◽  
Study Population

AbstractWe provide a comparison of general strategies for group testing in view of their application to medical diagnosis in the current COVID-19 pandemic. We find significant efficiency gaps between different group testing strategies in realistic scenarios for SARS-CoV-2 testing, highlighting the need for an informed decision of the pooling protocol depending on estimated prevalence, target specificity, and high- vs. low-risk population. For example, using one of the presented methods, all 1.47 million inhabitants of Munich, Germany, could be tested using only around 141 thousand tests if an infection rate up to 0.4% is assumed. Using 1 million tests, the 6.69 million inhabitants from the city of Rio de Janeiro, Brazil, could be tested as long as the infection rate does not exceed 1%. Altogether this work may help provide a basis for efficient upscaling of current testing procedures, fine grained towards the desired study population, e.g. cross-sectional versus health-care workers and adapted mixtures thereof. For comparative visualization and querying of the precomputed results we provide an interactive web application. The source code for computation is open and freely available.

scholarly journals Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies

2021 ◽  
Vol 9 ◽  
Author(s):  
Claudio M. Verdun ◽  
Tim Fuchs ◽  
Pavol Harar ◽  
Dennis Elbrächter ◽  
David S. Fischer ◽  
...  
Keyword(s):  
Infection Rate ◽  
Web Application ◽  
Diagnostic Testing ◽  
Group Testing ◽  
Population Heterogeneity ◽  
Testing Procedures ◽  
Testing Strategies ◽  
Specificity And Sensitivity ◽  
Novel Coronavirus ◽  
Dilution Effects

Background: Due to the ongoing COVID-19 pandemic, demand for diagnostic testing has increased drastically, resulting in shortages of necessary materials to conduct the tests and overwhelming the capacity of testing laboratories. The supply scarcity and capacity limits affect test administration: priority must be given to hospitalized patients and symptomatic individuals, which can prevent the identification of asymptomatic and presymptomatic individuals and hence effective tracking and tracing policies. We describe optimized group testing strategies applicable to SARS-CoV-2 tests in scenarios tailored to the current COVID-19 pandemic and assess significant gains compared to individual testing.Methods: We account for biochemically realistic scenarios in the context of dilution effects on SARS-CoV-2 samples and consider evidence on specificity and sensitivity of PCR-based tests for the novel coronavirus. Because of the current uncertainty and the temporal and spatial changes in the prevalence regime, we provide analysis for several realistic scenarios and propose fast and reliable strategies for massive testing procedures.Key Findings: We find significant efficiency gaps between different group testing strategies in realistic scenarios for SARS-CoV-2 testing, highlighting the need for an informed decision of the pooling protocol depending on estimated prevalence, target specificity, and high- vs. low-risk population. For example, using one of the presented methods, all 1.47 million inhabitants of Munich, Germany, could be tested using only around 141 thousand tests if the infection rate is below 0.4% is assumed. Using 1 million tests, the 6.69 million inhabitants from the city of Rio de Janeiro, Brazil, could be tested as long as the infection rate does not exceed 1%. Moreover, we provide an interactive web application, available at www.grouptexting.com, for visualizing the different strategies and designing pooling schemes according to specific prevalence scenarios and test configurations.Interpretation: Altogether, this work may help provide a basis for an efficient upscaling of current testing procedures, which takes the population heterogeneity into account and is fine-grained towards the desired study populations, e.g., mild/asymptomatic individuals vs. symptomatic ones but also mixtures thereof.Funding: German Science Foundation (DFG), German Federal Ministry of Education and Research (BMBF), Chan Zuckerberg Initiative DAF, and Austrian Science Fund (FWF).

COMPETITIVE GROUP TESTING AND LEARNING HIDDEN VERTEX COVERS WITH MINIMUM ADAPTIVITY

2010 ◽  
Vol 02 (03) ◽  
pp. 291-311 ◽  
Author(s):  
PETER DAMASCHKE ◽  
AZAM SHEIKH MUHAMMAD
Keyword(s):  
Vertex Cover ◽  
Group Testing ◽  
Success Probability ◽  
Optimal Number ◽  
Testing Strategies ◽  
Biological Network Analysis ◽  
Open Questions ◽  
Edge Group ◽  
Two Stages ◽  
Randomized Search

Suppose that we are given a set of n elements d of which have a property called defective. A group test can check for any subset, called a pool, whether it contains a defective. It is known that a nearly optimal number of O(d log (n/d)) pools in two stages (where tests within a stage are done in parallel) are sufficient, but then the searcher must know d in advance. Here we explore group testing strategies that use a nearly optimal number of pools and a few stages although d is not known beforehand. We prove a lower bound of Ω( log d/ log log d) stages and more general pools versus stages tradeoff. This is almost tight, since O( log d) stages are sufficient for a strategy with O(d log n) pools. As opposed to this negative result, we devise a randomized strategy using O(d log (n/d)) pools in three stages, with any desired success probability 1-ϵ. With some additional measures even two stages are enough. Open questions concern the optimal constant factors and practical implications. A related problem motivated by biological network analysis is to learn hidden vertex covers of a small size k in unknown graphs by edge group tests. (Does a given subset of vertices contain an edge?) We give a one-stage strategy using O(k3 log n) pools, with any parameterized algorithm for vertex cover enumeration as a decoder. During the course of this work we also provide a classification of types of randomized search strategies in general.

scholarly journals A network-based group testing strategy for colleges

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Alex Zhao ◽  
Kavin Kumaravel ◽  
Emanuele Massaro ◽  
Marta Gonzalez
Keyword(s):  
Group Testing ◽  
Local Information ◽  
Data Driven ◽  
Disease Spread ◽  
Transmission Network ◽  
Testing Strategy ◽  
Vital Importance ◽  
Testing Method ◽  
College Towns ◽  
Testing Strategies

AbstractGroup testing has recently become a matter of vital importance for efficiently and rapidly identifying the spread of Covid-19. In particular, we focus on college towns due to their density, observability, and significance for school reopenings. We propose a novel group testing strategy which requires only local information about the underlying transmission network. By using cellphone data from over 190,000 agents, we construct a mobility network and run extensive data-driven simulations to evaluate the efficacy of four different testing strategies. Our results demonstrate that our group testing method is more effective than three other baseline strategies for reducing disease spread with fewer tests.

scholarly journals Analysis and Applications of Adaptive Group Testing Methods for COVID-19

2020 ◽  
Author(s):  
Cassidy Mentus ◽  
Martin Romeo ◽  
Christian DiPaola
Keyword(s):  
Nursing Homes ◽  
Group Testing ◽  
Entire Group ◽  
Lower Prevalence ◽  
Rt Pcr ◽  
Testing Methods ◽  
Clinical Screening ◽  
Cruise Ships ◽  
Testing Strategies ◽  
Binary Splitting

AbstractTesting strategies for Covid-19 to maximize number of people tested are urgently needed. Recently, it has been demonstrated that RT-PCR has the sensitivity to detect one positive case in a mixed sample of 32 cases [12], In this paper we propose adaptive group testing strategies based on generalized binary splitting (CBS) [5], where we restrict the group test to the largest group that can be used. The method starts by choosing a group from the population to be tested, performing a test on the combined sample from the entire group, and progressively splitting the group further into subgroups. Compared to individual testing at 4% prevalence, we save 74%; at 1% we save 91%; and at .1% we save 98% of tests. We analyze the number of times each sample is used and show that the method is still efficient if we resort to testing a case individually if the sample is running low.In addition we recommend clinical screening to filter out individuals with symptoms and show this leaves us with a population with lower prevalence. Our approach is particularly applicable to vulnerable confined populations such as nursing homes, prisons, military ships and cruise ships.

scholarly journals Pooled Testing and Its Applications in the COVID-19 Pandemic

2021 ◽  
pp. 217-249
Author(s):  
Matthew Aldridge ◽  
David Ellis
Keyword(s):  
Standard Method ◽  
Group Testing ◽  
Two Stage ◽  
Pooled Testing ◽  
Testing Strategies ◽  
Pooling Strategies ◽  
Pooled Samples

AbstractWhen testing for a disease such as COVID-19, the standard method is individual testing: we take a sample from each individual and test these samples separately. An alternative is pooled testing (or ‘group testing’), where samples are mixed together in different pools, and those pooled samples are tested. When the prevalence of the disease is low and the accuracy of the test is fairly high, pooled testing strategies can be more efficient than individual testing. In this chapter, we discuss the mathematics of pooled testing and its uses during pandemics, in particular the COVID-19 pandemic. We analyse some one- and two-stage pooling strategies under perfect and imperfect tests, and consider the practical issues in the application of such protocols.

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