scholarly journals Group Testing Large Populations for SARS-CoV-2

2021 ◽  
Author(s):  
Hooman Zabeti ◽  
Nick Dexter ◽  
Ivan Lau ◽  
Leonhardt Unruh ◽  
Ben Adcock ◽  
...  
Keyword(s):  
Group Testing ◽  
Small Error ◽  
Noise Sources ◽  
Dominant Type ◽  
Original Proposal ◽  
Testing Strategies ◽  
Large Populations ◽  
Linear Programming Formulation ◽  
Syphilis Screening ◽  
Modular Platform

Group testing, the testing paradigm which combines multiple samples within a single test, was introduced in 1943 by Robert Dorfman. Since its original proposal for syphilis screening, group testing has been applied in domains such as fault identification in electrical and computer networks, machine learning, data mining, and cryptography. TheSARS-CoV-2 pandemic has led to proposals for using group testing in its original context of identifying infected individuals in a population with few tests. Studies suggest that non-adaptive group testing - in which all the tests are determined in advance - for SARS-CoV-2could help save 20% to 90% of tests depending on the prevalence. However, no systematic approach for comparing different non-adaptive group testing strategies currently exists. In this paper we develop a software platform for evaluating non-adaptive group testing strategies in both a noiseless setting and in the presence of realistic noise sources, modelled on published experimental observations, which makes them applicable to polymerase chain reaction (PCR) tests, the dominant type of tests for SARS-CoV-2. This modular platform can be used with a variety of group testing designs and decoding algorithms. We use it to evaluate the performance of near-doubly-regular designs and a decoding algorithm based on an integer linear programming formulation, both of which are known to be optimal in some regimes. We find savings between 40% and 91% of tests for prevalences up to 10% when a small error (below 5%) is allowed. We also find that the performance degrades gracefully with noise. We expect our modular, user-friendly, publicly available platform to facilitate empirical research into non-adaptive group testing for SARS-CoV-2.

scholarly journals Audit of antenatal screening for syphilis and HIV in migrant and refugee women on the Thai-Myanmar border: a descriptive study

F1000Research ◽  
2015 ◽  
Vol 3 ◽  
pp. 123 ◽  
Author(s):  
Rose McGready ◽  
Joy Kang ◽  
Isabella Watts ◽  
Mary Ellen G Tyrosvoutis ◽  
Miriam B. Torchinsky ◽  
...  
Keyword(s):  
Descriptive Study ◽  
First Year ◽  
Antenatal Screening ◽  
Testing Strategies ◽  
Syphilis Testing ◽  
Increased Risk ◽  
Syphilis Screening ◽  
Potential Impact ◽  
Low Prevalence ◽  
Positive Results

Objective: The antenatal prevalence of syphilis and HIV/AIDS in migrants and refugees is poorly documented. The aim of this study was to audit the first year of routine syphilis screening in the same population and reassess the trends in HIV rates.Methods: From August 2012 to July 2013, 3600 pregnant women were screened for HIV (ELISA) and syphilis (VDRL with TPHA confirmation) at clinics along the Thai-Myanmar border.Results: Seroprevalence for HIV 0.47% (95% CI 0.30-0.76) (17/3,599), and syphilis 0.39% (95% CI 0.23-0.65) (14/3,592), were low. Syphilis was significantly lower in refugees (0.07% 95% CI 0.01-0.38) (1/1,469), than in migrants (0.61% 95% CI 0.36-1.04) (13/2,123). The three active (VDRL≥1:8 and TPHA reactive) syphilis cases with VDRL titres of 1:32 were easy to counsel and treat. Women with low VDRL titres (>75% were < 1:8) and TPHA reactive results, in the absence of symptoms and both the woman and her husband having only one sexual partner in their lifetime, and the inability to determine the true cause of the positive results presented ethical difficulties for counsellors.Conclusion: As HIV and syphilis testing becomes available in more and more settings, the potential impact of false positive results should be considered, especially in populations with low prevalence for these diseases. This uncertainty must be considered in order to counsel patients and partners accurately and safely about the results of these tests, without exposing women to increased risk for abuse or abandonment. Our findings highlight the complexities of counselling patients about these tests and the global need for more conclusive syphilis testing strategies.

scholarly journals Noisy Pooled PCR for Virus Testing

2020 ◽  
Author(s):  
Junan Zhu ◽  
Kristina Rivera ◽  
Dror Baron
Keyword(s):  
Message Passing ◽  
Communication Channel ◽  
Group Testing ◽  
Diagnostic Tools ◽  
Rt Pcr ◽  
Message Passing Algorithm ◽  
Large Populations ◽  
Testing Algorithms ◽  
Virus Testing ◽  
False Positives And Negatives

AbstractFast testing can help mitigate the coronavirus disease 2019 (COVID-19) pandemic. Despite their accuracy for single sample analysis, infectious diseases diagnostic tools, like RT-PCR, require substantial resources to test large populations. We develop a scalable approach for determining the viral status of pooled patient samples. Our approach converts group testing to a linear inverse problem, where false positives and negatives are interpreted as generated by a noisy communication channel, and a message passing algorithm estimates the illness status of patients. Numerical results reveal that our approach estimates patient illness using fewer pooled measurements than existing noisy group testing algorithms. Our approach can easily be extended to various applications, including where false negatives must be minimized. Finally, in a Utopian world we would have collaborated with RT-PCR experts; it is difficult to form such connections during a pandemic. We welcome new collaborators to reach out and help improve this work!

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).

BOUNDS FOR NONADAPTIVE GROUP TESTS TO ESTIMATE THE AMOUNT OF DEFECTIVES

2011 ◽  
Vol 03 (04) ◽  
pp. 517-536 ◽  
Author(s):  
PETER DAMASCHKE ◽  
AZAM SHEIKH MUHAMMAD
Keyword(s):  
Error Probability ◽  
Group Testing ◽  
Constant Factor ◽  
Constant Number ◽  
Constant Ratio ◽  
Information Theoretic ◽  
Small Constant ◽  
Test Number ◽  
Linear Programming Formulation ◽  
Two Stages

Group testing is the problem of finding d defectives in a set of n elements, by asking carefully chosen subsets (pools) whether they contain defectives. Strategies are preferred that use both a small number of tests close to the information-theoretic lower bound d log 2 n, and a small constant number of stages, where tests in every stage are done in parallel, in order to save time. They should even work if d is not known in advance. In fact, one can succeed with O(d log n) queries in two stages, if certain tests are randomized and a constant failure probability is allowed. An essential ingredient of such strategies is to get an estimate of d within a constant factor. This problem is also interesting in its own right. It can be solved with O( log n) randomized group tests of a certain type. We prove that Ω( log n) tests are also necessary, if elements for the pools are chosen independently. The proof builds upon an analysis of the influence of tests on the searcher's ability to distinguish between any two candidate numbers with a constant ratio. The next challenge is to get optimal constant factors in the O( log n) test number, depending on the prescribed error probability and the accuracy of d. We give practical methods to derive upper bound tradeoffs and conjecture that they are already close to optimal. One of them uses a linear programming formulation.

scholarly journals Sample pooling methods for efficient pathogen screening: Practical implications

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0236849
Author(s):  
Tara N. Furstenau ◽  
Jill H. Cocking ◽  
Crystal M. Hepp ◽  
Viacheslav Y. Fofanov
Keyword(s):  
Human Error ◽  
Large Scale ◽  
Group Testing ◽  
Computational Simulations ◽  
Theoretical Approaches ◽  
Testing Theory ◽  
Large Populations ◽  
Sample Pooling ◽  
Pathogen Screening ◽  
Practical Implications

Due to the large number of negative tests, individually screening large populations for rare pathogens can be wasteful and expensive. Sample pooling methods improve the efficiency of large-scale pathogen screening campaigns by reducing the number of tests and reagents required to accurately categorize positive and negative individuals. Such methods rely on group testing theory which mainly focuses on minimizing the total number of tests; however, many other practical concerns and tradeoffs must be considered when choosing an appropriate method for a given set of circumstances. Here we use computational simulations to determine how several theoretical approaches compare in terms of (a) the number of tests, to minimize costs and save reagents, (b) the number of sequential steps, to reduce the time it takes to complete the assay, (c) the number of samples per pool, to avoid the limits of detection, (d) simplicity, to reduce the risk of human error, and (e) robustness, to poor estimates of the number of positive samples. We found that established methods often perform very well in one area but very poorly in others. Therefore, we introduce and validate a new method which performs fairly well across each of the above criteria making it a good general use approach.

scholarly journals Noisy Pooled PCR for Virus Testing

2020 ◽  
Author(s):  
Junan Zhu ◽  
Kristina Rivera ◽  
Dror Baron
Keyword(s):  
Message Passing ◽  
Communication Channel ◽  
Group Testing ◽  
Diagnostic Tools ◽  
Rt Pcr ◽  
Message Passing Algorithm ◽  
Large Populations ◽  
Testing Algorithms ◽  
Virus Testing ◽  
False Positives And Negatives

AbstractFast testing can help mitigate the coronavirus disease 2019 (COVID-19) pandemic. Despite their accuracy for single sample analysis, infectious diseases diagnostic tools, like RT-PCR, require substantial resources to test large populations. We develop a scalable approach for determining the viral status of pooled patient samples. Our approach converts group testing to a linear inverse problem, where false positives and negatives are interpreted as generated by a noisy communication channel, and a message passing algorithm estimates the illness status of patients. Numerical results reveal that our approach estimates patient illness using fewer pooled measurements than existing noisy group testing algorithms. Our approach can easily be extended to various applications, including where false negatives must be minimized. Finally, in a Utopian world we would have collaborated with RT-PCR experts; it is difficult to form such connections during a pandemic. We welcome new collaborators to reach out and help improve this work!

Sign in / Sign up

Export Citation Format

Share Document