scholarly journals Group Testing with Homophily to Curb Epidemics with Asymptomatic Carriers

2020 ◽  
Author(s):  
Louis-Marie Harpedanne de Belleville
Keyword(s):  
Interpersonal Relations ◽  
Group Testing ◽  
Testing Strategy ◽  
False Negatives ◽  
Information Theoretic ◽  
Asymptomatic Carriers ◽  
Homogeneity Assumption ◽  
Recent Advances ◽  
Multiple Step

SummaryContagion happens through heterogeneous interpersonal relations (homophily) which induce contamination clusters. Group testing is increasingly recognized as necessary to fight the asymptomatic transmission of the COVID-19. Still, it is plagued by false negatives. Homophily can be taken into account to design test pools that encompass potential contamination clusters. I show that this makes it possible to overcome the usual information-theoretic limits of group testing, which are based on an implicit homogeneity assumption. Even more interestingly, a multiple-step testing strategy combining this approach with advanced complementary exams for all individuals in pools identified as positive identifies asymptomatic carriers who would be missed even by costly exhaustive individual tests. Recent advances in group testing have brought large gains in efficiency, but within the bounds of the above cited information-theoretic limits, and without tackling the false negatives issue which is crucial for COVID-19. Homophily has been considered in the contagion literature already, but not in order to improve group testing.

scholarly journals Pooled testing with replication as a mass testing strategy for the COVID-19 pandemics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Julius Žilinskas ◽  
Algirdas Lančinskas ◽  
Mario R. Guarracino
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Group Testing ◽  
Testing Strategy ◽  
Testing Technique ◽  
Testing Procedures ◽  
Pooled Testing ◽  
Replication Scheme ◽  
Speed Up ◽  
Multiple Patients

AbstractDuring the COVID-19 pandemic it is essential to test as many people as possible, in order to detect early outbreaks of the infection. Present testing solutions are based on the extraction of RNA from patients using oropharyngeal and nasopharyngeal swabs, and then testing with real-time PCR for the presence of specific RNA filaments identifying the virus. This approach is limited by the availability of reactants, trained technicians and laboratories. One of the ways to speed up the testing procedures is a group testing, where the swabs of multiple patients are grouped together and tested. In this paper we propose to use the group testing technique in conjunction with an advanced replication scheme in which each patient is allocated in two or more groups to reduce the total numbers of tests and to allow testing of even larger numbers of people. Under mild assumptions, a 13 ×  average reduction of tests can be achieved compared to individual testing without delay in time.

scholarly journals Efficient high throughput SARS-CoV-2 testing to detect asymptomatic carriers

2020 ◽  
Author(s):  
Noam Shental ◽  
Shlomia Levy ◽  
Vered Wuvshet ◽  
Shosh Skorniakov ◽  
Yonat Shemer-Avni ◽  
...  
Keyword(s):  
Diagnostic Testing ◽  
Group Testing ◽  
Fold Increase ◽  
Effective Control ◽  
Large Set ◽  
Asymptomatic Carriers ◽  
Viral Shedding ◽  
Major Bottleneck ◽  
Polymerase Chain ◽  
Combinatorial Pooling

AbstractThe COVID-19 pandemic is rapidly spreading throughout the world. Recent reports suggest that 10-30% of SARS-CoV-2 infected patients are asymptomatic. Other studies report that some subjects have significant viral shedding prior to symptom onset. Since both asymptomatic and pre-symptomatic subjects can spread the disease, identifying such individuals is critical for effective control of the SARS-CoV-2 pandemic. Therefore, there is an urgent need to increase diagnostic testing capabilities in order to also screen asymptomatic carriers. In fact, such tests will be routinely required until a vaccine is developed. Yet, a major bottleneck of managing the COVID-19 pandemic in many countries is diagnostic testing, due to limited laboratory capabilities as well as limited access to genome-extraction and Polymerase Chain Reaction (PCR) reagents. We developed P-BEST - a method for Pooling-Based Efficient SARS-CoV-2 Testing, using a non-adaptive group-testing approach, which significantly reduces the number of tests required to identify all positive subjects within a large set of samples. Instead of testing each sample separately, samples are pooled into groups and each pool is tested for SARS-CoV-2 using the standard clinically approved PCR-based diagnostic assay. Each sample is part of multiple pools, using a combinatorial pooling strategy based on compressed sensing designed for maximizing the ability to identify all positive individuals. We evaluated P-BEST using leftover samples that were previously clinically tested for COVID-19. In our current proof-of-concept study we pooled 384 patient samples into 48 pools providing an 8-fold increase in testing efficiency. Five sets of 384 samples, containing 1-5 positive carriers were screened and all positive carriers in each set were correctly identified. P-BEST provides an efficient and easy-to-implement solution for increasing testing capacity that will work with any clinically approved genome-extraction and PCR-based diagnostic methodologies.

scholarly journals Pooled testing with replication: a mass testing strategy for the COVID-19 pandemics

2020 ◽  
Author(s):  
Julius Žilinskas ◽  
Algirdas Lančinskas ◽  
Mario R. Guarracino
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Group Testing ◽  
Antiviral Drugs ◽  
Testing Strategy ◽  
Testing Procedures ◽  
Pooled Testing ◽  
Replication Scheme ◽  
Speed Up ◽  
Multiple Patients

AbstractIn absence of a vaccine or antiviral drugs for the COVID-19 pandemic, it becomes urgent to test for positiveness to the virus as many people as possible, in order to detect early outbreaks of the infection. Present testing solutions are based on the extraction of RNA from patients using oropharyngeal (OP) and nasopharyngeal (NP) swabs, and then testing with real-time PCR for the presence of specific RNA filaments identifying the virus. This approach is limited by the availability of reactants, trained technicians and laboratories. To speed up the testing procedures, some attempts have been done on group testing, which means that the swabs of multiple patients are grouped together and tested. Here we propose to use this technique in conjunction with a combinatorial replication scheme in which each patient is allocated in two or more groups to reduce total numbers of tests and to allow testing of even larger numbers of people. Under mild assumptions, a 13× average reduction of tests can be achieved.

scholarly journals A Non-Adaptive Combinatorial Group Testing Strategy to Facilitate Healthcare Worker Screening During the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Outbreak

2020 ◽  
Author(s):  
John Henry McDermott ◽  
Duncan Stoddard ◽  
Peter Woolf ◽  
Jamie M Ellingford ◽  
David Gokhale ◽  
...  
Keyword(s):  
Web Application ◽  
Group Testing ◽  
Return Of Results ◽  
Testing Strategy ◽  
Healthcare Facilities ◽  
Staff Members ◽  
High Prevalence ◽  
Combinatorial Group Testing ◽  
Low Prevalence ◽  
Combinatorial Group

Background: Regular SARS-CoV-2 testing of healthcare workers (HCWs) has been proposed to prevent healthcare facilities becoming persistent reservoirs of infectivity. Using monoplex testing, widespread screening would be prohibitively expensive, and throughput may not meet demand. We propose a non-adaptive combinatorial (NAC) group-testing strategy to increase throughput and facilitate rapid turnaround via a single round of testing. Methods: NAC matrices were constructed for sample sizes of 700, 350 and 250 with replicates of 2, 4 and 5, respectively. Matrix performance was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1-10%, with each simulation ran for 10,000 iterations. Outcomes included the proportions of re-tests required and the proportion of true negatives identified. NAC matrices were compared to Dorfman Sequential (DS) approaches. A web application (www.samplepooling.com) was designed to decode results. Findings: NAC matrices performed well at low prevalence levels with an average number of 585 tests saved per assay in the n=700 matrix at a 1% prevalence. As prevalence increased, matrix performance deteriorated with n=250 most tolerant. In simulations of low to medium (0.1%-3%) prevalence levels all NAC matrices were superior, as measured by fewer repeated tests required, to the DS approaches. At very high prevalence levels (10%) the DS matrix was marginally superior, however both group testing approaches performed poorly at high prevalence levels. Interpretation: This testing strategy maximises the proportion of samples resolved after a single round of testing, allowing prompt return of results to staff members. Using the methodology described here, laboratories can adapt their testing scheme based on required throughput and the current population prevalence, facilitating a data-driven testing strategy.

Techniques for Multiple Watermarking

2010 ◽  
pp. 324-343
Author(s):  
Abdellatif Zaidi
Keyword(s):  
Information Theory ◽  
Theoretical Foundation ◽  
Information Theoretic ◽  
Design And Optimization ◽  
Multiple Watermarking ◽  
Recent Advances ◽  
New Findings ◽  
Multiple Watermarks ◽  
Multiuser Information Theory ◽  
Host Signal

The watermarking problem is relatively well understood in the single watermark case, but it lacks theoretical foundation in the multiple watermarks case. The goal of this chapter is to provide important technical insights as well as intuitive and well developed discussions onto how multiple watermarks can be embedded efficiently into the same host signal. The authors adopt communication and information theoretic inclinations, and they argue that this problem has tight relationship to conventional multiuser information theory. Then they show that by virtue of this tight relationship design and optimization of algorithms for multiple watermarking applications can greatly benefit from recent advances and new findings in multiuser information theory.

scholarly journals Information-Theoretic and Algorithmic Thresholds for Group Testing

2020 ◽  
Vol 66 (12) ◽  
pp. 7911-7928
Author(s):  
Amin Coja-Oghlan ◽  
Oliver Gebhard ◽  
Max Hahn-Klimroth ◽  
Philipp Loick
Keyword(s):  
Group Testing ◽  
Information Theoretic

scholarly journals Group testing as a strategy for COVID-19 epidemiological monitoring and community surveillance

2021 ◽  
Vol 17 (3) ◽  
pp. e1008726
Author(s):  
Vincent Brault ◽  
Bastien Mallein ◽  
Jean-François Rupprecht
Keyword(s):  
Viral Load ◽  
Statistical Model ◽  
Large Scale ◽  
Infected Individual ◽  
Group Testing ◽  
Epidemic Outbreak ◽  
False Negatives ◽  
Testing Method ◽  
Epidemiological Monitoring ◽  
Sample Pooling

We propose an analysis and applications of sample pooling to the epidemiologic monitoring of COVID-19. We first introduce a model of the RT-qPCR process used to test for the presence of virus in a sample and construct a statistical model for the viral load in a typical infected individual inspired by large-scale clinical datasets. We present an application of group testing for the prevention of epidemic outbreak in closed connected communities. We then propose a method for the measure of the prevalence in a population taking into account the increased number of false negatives associated with the group testing method.

scholarly journals Information-Theoretic Methods in Deep Neural Networks: Recent Advances and Emerging Opportunities

2021 ◽  
Author(s):  
Shujian Yu ◽  
Luis Sanchez Giraldo ◽  
Jose Principe
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Learning Problems ◽  
Information Theoretic ◽  
Information Theoretic Learning ◽  
Recent Advances ◽  
Recent Developments ◽  
Learning Principles ◽  
Review Current ◽  
Information Theoretic Methods

We present a review on the recent advances and emerging opportunities around the theme of analyzing deep neural networks (DNNs) with information-theoretic methods. We first discuss popular information-theoretic quantities and their estimators. We then introduce recent developments on information-theoretic learning principles (e.g., loss functions, regularizers and objectives) and their parameterization with DNNs. We finally briefly review current usages of information-theoretic concepts in a few modern machine learning problems and list a few emerging opportunities.

Probability models for detecting transgenic plants

2008 ◽  
Vol 18 (2) ◽  
pp. 77-89 ◽  
Author(s):  
Carlos M. Hernández-Suárez ◽  
Osval A. Montesinos-López ◽  
Graham McLaren ◽  
José Crossa
Keyword(s):  
Transgenic Plants ◽  
Sample Size ◽  
High Probability ◽  
Probabilistic Models ◽  
Negative Binomial ◽  
Low Cost ◽  
Group Testing ◽  
Probability Models ◽  
False Negatives ◽  
Testing Method

AbstractWhen detecting the adventitious presence of transgenic plants (AP), it is important to use an appropriate testing method in the laboratory. Dorfman's group testing method is effective for reducing the number of laboratory analyses, but does not consider the case where AP is diluted below the sensitivity of the analyses, which causes the rate of false negatives to increase. The objective of this study is to propose binomial and negative binomial probabilistic models for determining the required sample size (n), number of pools (g), and size of the pool (k) for detecting individuals possessing AP with a probability ≥ (1 − α) (for a small α) given: (1) pool size (k); (2) estimated proportion of individuals with AP in the population (p); (3) concentration of the trait of interest (AP) in individual seeds (w); and (4) detection limit of the test (c) (AP concentration in a pool below which it cannot be detected). The proposed models consider the different rates of false positives (δ) and false negatives (λ), and the assessment of consumer and producer risks. Results have shown that when using the negative binomial, a required sample size n can be determined that guarantees a high probability that m individuals or g pools containing AP will be found. The pools formed have an optimum size, such that one element with AP will be detected at a low cost. The negative binomial distribution should be used when it is known that the proportion of individuals with AP in the population is p < 0.1; thus, it is guaranteed that m individuals or g pools of individuals with AP will be detected with high probability.

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