Two-Stage Adaptive Pooling with RT-qPCR for COVID-19 Screening

Heidarzadeh, Anoosheh; Narayanan, Krishna R.

doi:10.1101/2020.07.05.20146936

Cited by 13 publications

(13 citation statements)

References 16 publications

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“…Addressing these questions calls for an improved understanding of community structure, outbreak locations, and individual lifestyles. [ 7,8 ] Due to the scale of the COVID‐19 epidemic, an additional burden has been placed on traditional testing sites, such as hospitals, emergency rooms, and walk‐in clinics, thereby challenging their safety. [ 9–11 ]…”

Section: Introductionmentioning

confidence: 99%

High‐Resolution Agent‐Based Modeling of COVID‐19 Spreading in a Small Town

Truszkowska

Behring

Hasanyan

et al. 2021

Advcd Theory and Sims

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Amid the ongoing COVID‐19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of “what‐if” scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent‐based modeling platform is proposed to simulate the spreading of COVID‐19 in small towns and cities, with a single‐individual resolution. The platform is validated on real data from New Rochelle, NY—one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID‐19. Unique to the model is the possibility to explore different testing approaches—in hospitals or drive‐through facilities—and vaccination strategies that could prioritize vulnerable groups. Decision‐making by public authorities could benefit from the model, for its fine‐grain resolution, open‐source nature, and wide range of features.

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Section: Introductionmentioning

confidence: 99%

High‐Resolution Agent‐Based Modeling of COVID‐19 Spreading in a Small Town

Truszkowska

Behring

Hasanyan

et al. 2021

Advcd Theory and Sims

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“…Sophisticated pooling schemes offering more efficient testing than the Dorfman scheme have been designed before 14,15 . Gosh and colleagues as well as Heidazardeh and Narayanan developed group testing schemes for RT-qPCR measurements of COVID-19 which use algorithms that also leverage soft information from the qPCR process to significantly improve Dorfman-style testing 16,17 . Thus it is clear that more complex testing algorithms will outcompete simple testing procedures.…”

Section: Discussionmentioning

confidence: 99%

24 People, one test: Boosting test efficiency using pooled serum antibody testing for SARS-CoV-2

Nessler

Franz

Meer

et al. 2020

Preprint

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Background: The global pandemic of COVID-19 (coronavirus disease 2019) is caused by the novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), with different prevalence rates across countries and regions. Dynamic testing strategies are mandatory to establish efficient mitigation strategies against the disease; to be cost effective, they should adapt to regional prevalences. Seroprevalence surveys that detect individuals who have mounted an immune response against COVID-19 will help to determine the total number of infections within a community and improve the epidemiological calculations of attack and case fatality rates of the virus. They will also inform about the percentage of a population that might be immune against re-infections. Methods: We developed a sensitive and specific cell-based assay to detect conformational SARS-CoV-2 spike (SARS-2-S) S1 antibodies in human serum, and have cross-evaluated this assay against two FDA-approved SARS-CoV-2 antibody assays. We performed pseudovirus neutralization assays to determine whether sera that were rated antibody-positive in our assay were able to specifically neutralize SARS-2-S. We pooled up to 24 sera and assessed the group testing performance of our cell-based assay. Group testing was further optimized by Monte Carlo like simulations and prospectively evaluated. Findings: Highly significant correlations could be established between our cell-based assay and commercial antibody tests for SARS-CoV-2. SARS-2-S S1 antibody-positive sera neutralized SARS-2-S but not SARS-S, and were sensitively and specifically detected in pools of 24 samples. Monte Carlo like simulations demonstrated that a simple two-step pooling scheme with fixed pool sizes performed at least equally as well as Dorfman's optimal testing across a wide range of antibody prevalences. Interpretation: We demonstrate that a cell-based assay for SARS-2-S S1 antibodies qualifies for group testing of neutralizing anti-SARS-2-S antibodies. The assay can be combined with an easily implemented algorithm which greatly expands the screening capacity to detect anti-SARS-2-S antibodies across a wide range of antibody prevalences. It will thus improve population serological testing in many countries.

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“…The ith measurement y i and the noisy measurement z i are given by y i = ∑ n j=1 a i, j x j and z i = y i ε i , respectively, where ε i 's are independent realizations of a random variable ε-taking values from R >0 according to a fixed and known probability distribution p ε . A detailed explanation about this noise model can be found in [13].…”

Section: Problem Setupmentioning

confidence: 99%

“…The weight profile must be chosen to obtain a good trade-off between the computational complexity of sensing/recovery algorithms and the false negative/positive rates. The weight profiles used in our simulations can be found in [13].…”

Section: Sensing Algorithm For Second Stagementioning

confidence: 99%

Two-Stage Adaptive Pooling with RT-QPCR for Covid-19 Screening

Heidarzadeh

Narayanan

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose two-stage adaptive pooling schemes, 2-STAP and 2-STAMP, for detecting COVID-19 using real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) test kits. Similar to the Tapestry scheme of Ghosh et al., the proposed schemes leverage soft information from the RT-qPCR process about the total viral load in the pool. This is in contrast to conventional group testing schemes where the measurements are Boolean. The proposed schemes provide higher testing throughput than the popularly used Dorfman's scheme. They also provide higher testing throughput, sensitivity and specificity than the state-of-the-art non-adaptive Tapestry scheme. The number of pipetting operations is lower than the Tapestry scheme, and is higher than that for the Dorfman's scheme. The proposed schemes can work with substantially smaller group sizes than non-adaptive schemes and are simple to describe. Monte-Carlo simulations using the statistical model in the work of Ghosh et al. (Tapestry) show that 10 infected people in a population of size 961 can be identified with 70.86 tests on the average with a sensitivity of 99.50% and specificity of 99.62%. This is 13.5x, 4.24x, and 1.3x the testing throughput of individual testing, Dorfman's testing, and the Tapestry scheme, respectively.

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Two-Stage Adaptive Pooling with RT-qPCR for COVID-19 Screening

Cited by 13 publications

References 16 publications

High‐Resolution Agent‐Based Modeling of COVID‐19 Spreading in a Small Town

High‐Resolution Agent‐Based Modeling of COVID‐19 Spreading in a Small Town

24 People, one test: Boosting test efficiency using pooled serum antibody testing for SARS-CoV-2

Two-Stage Adaptive Pooling with RT-QPCR for Covid-19 Screening

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