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

Disposable graphene-oxide screen-printed electrode integrated with portable device for detection of SARS-CoV-2 in clinical samples

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnosis is the need of the hour, as cases are persistently increasing, and new variants are constantly emerging. The ever-changing nature of the virus leading to multiple variants, has brought an imminent need for early, accurate and rapid detection methods. Herein, we have reported the design and fabrication of Screen-Printed Electrodes (SPEs) with graphene oxide (GO) as working electrode and modified with specific antibodies for SARS-CoV-2 Receptor Binding Domain (RBD). Flexibility of design, and portable nature has made SPEs the superior choice for electrochemical analysis. The developed immunosensor can detect RBD as low as 0.83 fM with long-term storage capacity. The fabricated SPEs immunosensor was tested using a miniaturized portable device and potentiostat on 100 patient nasopharyngeal samples and corroborated with RT-PCR data, displayed 94 % sensitivity. Additionally, the in-house developed polyclonal antibodies detected RBD antigen of the mutated Omicron variant of SARS-CoV-2 successfully. We have not observed any cross-reactivity/binding of the fabricated immunosensor with MERS (cross-reactive antigen) and Influenza A H1N1 (antigen sharing common symptoms). Hence, the developed SPEs sensor may be applied for bedside point-of-care diagnosis of SARS-CoV-2 using miniaturized portable device, in clinical samples.

Clinical performance of AI-integrated risk assessment pooling reveals cost savings even at high prevalence of COVID-19

Individual testing of samples is time- and cost-intensive, particularly during an ongoing pandemic. Better practical alternatives to individual testing can significantly decrease the burden of disease on the healthcare system. Herein, we presented the clinical validation of Segtnan™ on 3929 patients. Segtnan™ is available as a mobile application entailing an AI-integrated personalized risk assessment approach with a novel data-driven equation for pooling of biological samples. The AI was selected from a comparison between 15 machine learning classifiers (highest accuracy = 80.14%) and a feed-forward neural network with an accuracy of 81.38% in predicting the rRT-PCR test results based on a designed survey with minimal clinical questions. Furthermore, we derived a novel pool-size equation from the pooling data of 54 published original studies. The results demonstrated testing capacity increase of 750%, 60%, and 5% at prevalence rates of 0.05%, 22%, and 50%, respectively. Compared to Dorfman's method, our novel equation saved more tests significantly at high prevalence, i.e., 28% (p = 0.006), 40% (p = 0.00001), and 66% (p = 0.02). Lastly, we illustrated the feasibility of the Segtnan™ usage in clinically complex settings like emergency and psychiatric departments.

Bayesian group testing with dilution effects

A Bayesian framework for group testing under dilution effects has been developed, using lattice-based models. This work has particular relevance given the pressing public health need to enhance testing capacity for coronavirus disease 2019 and future pandemics, and the need for wide-scale and repeated testing for surveillance under constantly varying conditions. The proposed Bayesian approach allows for dilution effects in group testing and for general test response distributions beyond just binary outcomes. It is shown that even under strong dilution effects, an intuitive group testing selection rule that relies on the model order structure, referred to as the Bayesian halving algorithm, has attractive optimal convergence properties. Analogous look-ahead rules that can reduce the number of stages in classification by selecting several pooled tests at a time are proposed and evaluated as well. Group testing is demonstrated to provide great savings over individual testing in the number of tests needed, even for moderately high prevalence levels. However, there is a trade-off with higher number of testing stages, and increased variability. A web-based calculator is introduced to assist in weighing these factors and to guide decisions on when and how to pool under various conditions. High-performance distributed computing methods have also been implemented for considering larger pool sizes, when savings from group testing can be even more dramatic.

Magnetic Janus origami robot for cross-scale droplet omni-manipulation

The versatile manipulation of cross-scale droplets is essential in many fields. Magnetic excitation is widely used for droplet manipulation due to its distinguishing merits. However, facile magnetic actuation strategies are still lacked to realize versatile multiscale droplet manipulation. Here, a type of magnetically actuated Janus origami robot is readily fabricated for versatile cross-scale droplet manipulation including three-dimensional transport, merging, splitting, dispensing and release of daughter droplets, stirring and remote heating. The robot allows untethered droplet manipulation from ~3.2 nL to ~51.14 μL. It enables splitting of droplet, precise dispensing (minimum of ~3.2 nL) and release (minimum of ~30.2 nL) of daughter droplets. The combination of magnetically controlled rotation and photothermal properties further endows the robot with the ability to stir and heat droplets remotely. Finally, the application of the robot in polymerase chain reaction (PCR) is explored. The extraction and purification of nucleic acids can be successfully achieved.

Rapid Single-Round Pool Testing of Infectious Disease Enabled by Multicolor Digital Melting PCR

Pooled nucleic acid amplification test is a promising strategy to reduce cost and resources for screening large populations for infectious disease. However, the benefit of pooled testing is reversed when disease prevalence is high, because of the need to retest each sample to identify infected individual when a pool is positive. Split, Amplify, and Melt analysis of Pooled Assay (SAMPA) is presented, a multicolor digital melting PCR assay in nanoliter chambers that simultaneously identify infected individuals and quantify their viral loads in a single round of pooled testing. This is achieved by early sample tagging with unique barcodes and pooling, followed by single molecule barcode identification in a digital PCR platform using a highly multiplexed melt curve analysis strategy. The feasibility is demonstrated of SAMPA for quantitative unmixing and variant identification from pools of eight synthetic DNA and RNA samples corresponding to the N1 gene, as well as from heat-inactivated SARS-CoV-2 virus. Single round pooled testing of barcoded samples with SAMPA can be a valuable tool for rapid and scalable population testing of infectious disease.

MultiplexMS: A Mass Spectrometry-Based Multiplexing Strategy for Ultra-High-Throughput Analysis of Complex Mixtures

High-throughput chemical analysis of natural product mixtures lags behind developments in genome sequencing technologies and laboratory automation, leading to a disconnect between library-scale chemical and biological profiling that limits new molecule discovery. Here, we report a new orthogonal sample multiplexing strategy that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. Profiled pooled samples undergo subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using in silico experiments and demonstrated a high assignment precision (>97%) for large, pooled samples (r = 30), particularly for infrequently occurring metabolites of relevance in drug discovery applications. Requiring only 5% of the previously required MS acquisition time, this approach was repeated in a recent biological activity profiling study on 925 natural product extracts, leading to the rediscovery of all previously reported bioactive metabolites. This new method is compatible with MS data from any instrument vendor and is supported by an open-source software package: https://github.com/liningtonlab/MultiplexMS.

Diagnostics and analysis of SARS-CoV-2: current status, recent advances, challenges and perspectives

The disastrous spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has induced severe public healthcare issues and weakened the global economy significantly. Although SARS-CoV-2 infection is not as fatal as the initial outbreak, many infected victims suffer from long COVID. Therefore, rapid and large-scale testing is critical in managing patients and alleviating its transmission. Herein, we review the recent advances in techniques to detect SARS-CoV-2. The sensing principles are detailed together with their application domains and analytical performances. In addition, the advantages and limits of each method are discussed and analyzed. Besides molecular diagnostics and antigen and antibody tests, we also review neutralizing antibodies and emerging SARS-CoV-2 variants. Further, the characteristics of the mutational locations in the different variants with epidemiological features are summarized. Finally, the challenges and possible strategies are prospected to develop new assays to meet different diagnostic needs. Thus, this comprehensive and systematic review of SARS-CoV-2 detection technologies may provide insightful guidance and direction for developing tools for the diagnosis and analysis of SARS-CoV-2 to support public healthcare and effective long-term pandemic management and control.

CoVSense: Ultrasensitive Nucleocapsid Antigen Immunosensor for Rapid Clinical Detection of Wildtype and Variant SARS-CoV-2

The widespread accessibility of commercial/clinically-viable electrochemical diagnostic systems for rapid quantification of viral proteins demands translational/preclinical investigations. Here, Covid-Sense (CoVSense) antigen testing platform; an all-in-one electrochemical nano-immunosensor for sample-to-result, self-validated, and accurate quantification of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N)-proteins in clinical examinations is developed. The platform's sensing strips benefit from a highly-sensitive, nanostructured surface, created through the incorporation of carboxyl-functionalized graphene nanosheets, and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) conductive polymers, enhancing the overall conductivity of the system. The nanoengineered surface chemistry allows for compatible direct assembly of bioreceptor molecules. CoVSense offers an inexpensive (<$2 kit) and fast/digital response (<10 min), measured using a customized hand-held reader (<$25), enabling data-driven outbreak management. The sensor shows 95% clinical sensitivity and 100% specificity (Ct<25), and overall sensitivity of 91% for combined symptomatic/asymptomatic cohort with wildtype SARS-CoV-2 or B.1.1.7 variant (N = 105, nasal/throat samples). The sensor correlates the N-protein levels to viral load, detecting high Ct values of ≈35, with no sample preparation steps, while outperforming the commercial rapid antigen tests. The current translational technology fills the gap in the workflow of rapid, point-of-care, and accurate diagnosis of COVID-19.

Efficient large-scale screening of viral pathogens by fragment length identification of pooled nucleic acid samples (FLIPNAS)

The necessity for the large-scale screening of viral pathogens has been amply demonstrated during the COVID-19 pandemic. During this time, SARS-CoV-2 nucleic acid pooled testing, such as Dorfman-based group testing, was widely adopted in response to the sudden increased demand for detection. However, the current approach still necessitates the individual retesting of positive pools. Here, we established an efficient method termed the fragment-length identification of pooled nucleic acid samples (FLIPNAS), where all subsamples (n = 8) can be uniquely labelled and tested in a single-time detection among pools of samples. We used a novel and simple design of unique primers (UPs) to generate amplicons of unique lengths after reverse transcription and polymerase chain reaction to reach this aim. As a result, the unique lengths of the amplicons can be recognized and traced back to the corresponding UPs and specific samples. Our results demonstrated that FLIPNAS could recognize one to eight positive subsamples in a single test without retesting positive pools. The system also showed sufficient sensitivity for the mass monitoring of SARS-CoV-2 and no cross-reactivity against three common respiratory diseases. Moreover, the FLIPNAS results of 40 samples with a positive ratio of 7.8% were in 100% agreement with their individual detection results using the gold standard. Collectively, this study shows that the efficiency of nucleic acid pooling detection can be further improved by FLIPNAS, which can speed up testing and mitigate the urgent demand for resources.

Operational analysis for COVID-19 testing: Determining the risk from asymptomatic infections

Testing remains a key tool for managing health care and making health policy during the coronavirus pandemic, and it will probably be important in future pandemics. Because of false negative and false positive tests, the observed fraction of positive tests-the surface positivity-is generally different from the fraction of infected individuals (the incidence rate of the disease). In this paper a previous method for translating surface positivity to a point estimate for incidence rate, then to an appropriate range of values for the incidence rate consistent with the model and data (the test range), and finally to the risk (the probability of including one infected individual) associated with groups of different sizes is illustrated. The method is then extended to include asymptomatic infections. To do so, the process of testing is modeled using both analysis and Monte Carlo simulation. Doing so shows that it is possible to determine point estimates for the fraction of infected and symptomatic individuals, the fraction of uninfected and symptomatic individuals, and the ratio of infected asymptomatic individuals to infected symptomatic individuals. Inclusion of symptom status generalizes the test range from an interval to a region in the plane determined by the incidence rate and the ratio of asymptomatic to symptomatic infections; likelihood methods can be used to determine the contour of the rest region. Points on this contour can be used to compute the risk (defined as the probability of including one asymptomatic infected individual) in groups of different sizes. These results have operational implications that include: positivity rate is not incidence rate; symptom status at testing can provide valuable information about asymptomatic infections; collecting information on time since putative virus exposure at testing is valuable for determining point estimates and test ranges; risk is a graded (rather than binary) function of group size; and because the information provided by testing becomes more accurate with more tests but at a decreasing rate, it is possible to over-test fixed spatial regions. The paper concludes with limitations of the method and directions for future work.

Optimizing SARS-CoV-2 Pooled Testing Strategies Through Differentiated Pooling for Distinct Groups

Pooled testing has been successfully used to expand SARS-CoV-2 testing, especially in settings requiring high volumes of screening of lower-risk individuals, but efficiency of pooling declines as prevalence rises. We propose a differentiated pooling strategy that independently optimizes pool sizes for distinct groups with different probabilities of infection to further improve the efficiency of pooled testing. We compared the efficiency (results obtained per test kit used) of the differentiated strategy with a traditional pooling strategy in which all samples are processed using uniform pool sizes under a range of scenarios. For most scenarios, differentiated pooling is more efficient than traditional pooling. In scenarios examined here, an improvement in efficiency of up to 3.94 results per test kit could be obtained through differentiated versus traditional pooling, with more likely scenarios resulting in 0.12 to 0.61 additional results per kit. Under circumstances similar to those observed in a university setting, implementation of our strategy could result in an improvement in efficiency between 0.03 to 3.21 results per test kit. Our results can help identify settings, such as universities and workplaces, where differentiated pooling can conserve critical testing resources.

Effective matrix designs for COVID-19 group testing

BACKGROUND

Grouping samples with low prevalence of positives into pools and testing these pools can achieve considerable savings in testing resources compared with individual testing in the context of COVID-19. We review published pooling matrices, which encode the assignment of samples into pools and describe decoding algorithms, which decode individual samples from pools. Based on the findings we propose new one-round pooling designs with high compression that can efficiently be decoded by combinatorial algorithms. This expands the admissible parameter space for the construction of pooling matrices compared to current methods.

RESULTS

By arranging samples in a grid and using polynomials to construct pools, we develop direct formulas for an Algorithm (Polynomial Pools (PP)) to generate assignments of samples into pools. Designs from PP guarantee to correctly decode all samples with up to a specified number of positive samples. PP includes recent combinatorial methods for COVID-19, and enables new constructions that can result in more effective designs.

CONCLUSION

For low prevalences of COVID-19, group tests can save resources when compared to individual testing. Constructions from the recent literature on combinatorial methods have gaps with respect to the designs that are available. We develop a method (PP), which generalizes previous constructions and enables new designs that can be advantageous in various situations.

Group Testing with Blocks of Positives and Inhibitors

The main goal of group testing is to identify a small number of specific items among a large population of items. In this paper, we consider specific items as positives and inhibitors and non-specific items as negatives. In particular, we consider a novel model called group testing with blocks of positives and inhibitors. A test on a subset of items is positive if the subset contains at least one positive and does not contain any inhibitors, and it is negative otherwise. In this model, the input items are linearly ordered, and the positives and inhibitors are subsets of small blocks (at unknown locations) of consecutive items over that order. We also consider two specific instantiations of this model. The first instantiation is that model that contains a single block of consecutive items consisting of exactly known numbers of positives and inhibitors. The second instantiation is the model that contains a single block of consecutive items containing known numbers of positives and inhibitors. Our contribution is to propose efficient encoding and decoding schemes such that the numbers of tests used to identify only positives or both positives and inhibitors are less than the ones in the state-of-the-art schemes. Moreover, the decoding times mostly scale to the numbers of tests that are significantly smaller than the state-of-the-art ones, which scale to both the number of tests and the number of items.

An improvement of current hypercube pooling PCR tests for SARS-CoV-2 detection

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic can be effectively controlled by rapid and accurate identification of SARS-CoV-2-infected cases through large-scale screening. Hypercube pooling polymerase chain reaction (PCR) is frequently used as a pooling technique because of its high speed and efficiency. We attempted to implement the hypercube pooling strategy and found it had a large quantization effect. This raised two questions: is hypercube pooling with edge = 3 actually the optimal strategy? If not, what is the best edge and dimension? We used a C++ program to calculate the expected number of PCR tests per patient for different values of prevalence, edge, and dimension. The results showed that every edge had a best performance range. Then, using C++ again, we created a program to calculate the optimal edge and dimension required for pooling samples when entering prevalence into our program. Our program will be provided as freeware in the hope that it can help governments fight the SARS-CoV-2 pandemic.

Optimal group testing strategy for the mass screening of SARS-CoV-2

We analyze the group testing strategy that maximizes the efficiency of the SARS-CoV-2 screening test while ensuring its effectiveness, where the effectiveness of group testing guarantees that negative results from pooled samples can be considered presumptive negative. Two aspects of test efficiency are considered, one concerning the maximization of the welfare throughput and the other concerning the maximization of the identification rate (namely, identifying as many infected individuals as possible). We show that compared with individual testing, group testing leads to a higher probability of false negative results but a lower probability of false positive results. To ensure the test effectiveness, both the group size and the prevalence of SARS-CoV-2 must be below certain respective thresholds. To achieve test efficiency that concerns either the welfare throughput maximization or the identification rate maximization, the optimal group size is jointly determined by the test accuracy parameters, the infection prevalence rate, and the relative importance of identifying infected subjects. We also show that the optimal group size that maximizes the welfare throughput is weakly smaller than the one that maximizes the identification rate.

Use of compressed sensing to expedite high-throughput diagnostic testing for COVID-19 and beyond

The rapid spread of SARS-CoV-2 has placed a significant burden on public health systems to provide swift and accurate diagnostic testing highlighting the critical need for innovative testing approaches for future pandemics. In this study, we present a novel sample pooling procedure based on compressed sensing theory to accurately identify virally infected patients at high prevalence rates utilizing an innovative viral RNA extraction process to minimize sample dilution. At prevalence rates ranging from 0-14.3%, the number of tests required to identify the infection status of all patients was reduced by 69.26% as compared to conventional testing in primary human SARS-CoV-2 nasopharyngeal swabs and a coronavirus model system. Our method provided quantification of individual sample viral load within a pool as well as a binary positive-negative result. Additionally, our modified pooling and RNA extraction process minimized sample dilution which remained constant as pool sizes increased. Compressed sensing can be adapted to a wide variety of diagnostic testing applications to increase throughput for routine laboratory testing as well as a means to increase testing capacity to combat future pandemics.

Modeling the optimization of COVID-19 pooled testing: How many samples can be included in a single test?

Objectives

This study tries to answer the crucial question of how many biological samples can be optimally included in a single test for COVID-19 pooled testing.

Methods

It builds a novel theoretical model which links the local population to be tested in a region, the number of biological samples included in a single test, the “attitude” toward resource cost saving and time taken in a single test, as well as the corresponding resource cost function and time function, together. The numerical simulation results are then used to formulate the resource cost function as well as the time function. Finally, a loss function to be minimized is constructed and the optimal number of samples included is calculated.

Results

In a numerical example, we consider a region of 1 million population which needs to be tested for the infection of COVID-19. The solution calculates the optimal number of biological samples included in a single test as 4.254 when the time taken is given the weight of 50% under the infection probability of 10%. Other combinations of numerical results are also presented.

Conclusions

As we can see in our simulation results, given the infection probability at 10%, setting the number of biological samples included in a single test (in the integer level) at [4,6] is reasonable for a wide range of the subjective attitude between time and resource costs. Therefore, in the current practice, 5-mixed samples would sound better than the commonly used 10-mixed samples.

Epidemic management and control through risk-dependent individual contact interventions

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.

During the ongoing COVID-19 pandemic, exposure notification apps have been developed to scale up manual contact tracing. The apps use proximity data from mobile devices to automate notifying direct contacts of an infection source. The information they provide is limited because users receive only rare and binary alerts. Here we present network data assimilation (DA) as a new digital approach to epidemic management and control. Network DA uses the same data as exposure notification apps but uses it more effectively to provide frequently updated individual risk assessments to users.

Network DA is based on automated learning about individuals’ risk of exposure and infection from crowd-sourced health data and proximity data. The data are aggregated with models of disease transmission to produce statistical assessments of users’ risks. In an extensive simulation study of the COVID-19 epidemic in New York City (NYC), we show that network DA with diagnostic testing achieves epidemic control with fewer than half the deaths that occurred during NYC’s lockdown, while isolating a far smaller fraction of the population (typically only 5–10% of the population at any given time).

Implemented at scale, then, network DA has the potential to effectively control epidemics while minimizing economic and social disruption.

Mitigation of SARS-CoV-2 transmission at a large public university

In Fall 2020, universities saw extensive transmission of SARS-CoV-2 among their populations, threatening health of the university and surrounding communities, and viability of in-person instruction. Here we report a case study at the University of Illinois at Urbana-Champaign, where a multimodal “SHIELD: Target, Test, and Tell” program, with other non-pharmaceutical interventions, was employed to keep classrooms and laboratories open. The program included epidemiological modeling and surveillance, fast/frequent testing using a novel low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD, and digital tools for communication and compliance. In Fall 2020, we performed >1,000,000 covidSHIELD tests, positivity rates remained low, we had zero COVID-19-related hospitalizations or deaths amongst our university community, and mortality in the surrounding Champaign County was reduced more than 4-fold relative to expected. This case study shows that fast/frequent testing and other interventions mitigated transmission of SARS-CoV-2 at a large public university.

Mitigation of SARS-CoV-2 transmission at a large public university

In Fall 2020, universities saw extensive transmission of SARS-CoV-2 among their populations, threatening health of the university and surrounding communities, and viability of in-person instruction. Here we report a case study at the University of Illinois at Urbana-Champaign, where a multimodal “SHIELD: Target, Test, and Tell” program, with other non-pharmaceutical interventions, was employed to keep classrooms and laboratories open. The program included epidemiological modeling and surveillance, fast/frequent testing using a novel low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD, and digital tools for communication and compliance. In Fall 2020, we performed >1,000,000 covidSHIELD tests, positivity rates remained low, we had zero COVID-19-related hospitalizations or deaths amongst our university community, and mortality in the surrounding Champaign County was reduced more than 4-fold relative to expected. This case study shows that fast/frequent testing and other interventions mitigated transmission of SARS-CoV-2 at a large public university.

Safely opening university campuses has been a major challenge during the COVID-19 pandemic. Here, the authors describe a program of public health measures employed at a university in the United States which, combined with other non-pharmaceutical interventions, allowed the university to stay open in fall 2020 with limited evidence of transmission.

Effective bubble-based testing for SARS-CoV-2 using swab-pooling

OBJECTIVES

Despite the success in developing COVID-19 vaccines, containment of the disease is obstructed worldwide by vaccine production bottlenecks, logistics hurdles, vaccine refusal, transmission through unvaccinated children, and the appearance of new viral variants. This underscores the need for effective strategies for identifying carriers/patients, which was the main aim of this study.

METHODS

We present a bubble-based PCR testing approach using swab-pooling into lysis buffer. A bubble is a cluster of people who can be periodically tested for SARS-CoV-2 by swab-pooling. A positive test of a pool mandates quarantining each of its members, who are then individually tested while in isolation to identify the carrier(s) for further epidemiological contact tracing.

RESULTS

We tested an overall sample of 25 831 individuals, divided into 1273 bubbles, with an average size of 20.3 ± 7.7 swabs/test tube, obtaining for all pools (≤37 swabs/pool) a specificity of 97.5% (lower bound 96.6%) and a sensitivity of 86.3% (lower bound 78.2%) and a post hoc analyzed sensitivity of 94.6% (lower bound 86.7%) and a specificity of 97.2% (lower bound 96.2%) in pools with ≤25 swabs, relative to individual testing.

DISCUSSION

This approach offers a significant scale-up in sampling and testing throughput and savings in testing cost, without reducing sensitivity or affecting the standard PCR testing laboratory routine. It can be used in school classes, airplanes, hospitals, military units, and workplaces, and may be applicable to future pandemics.

Toward a next-generation diagnostic tool: A review on emerging isothermal nucleic acid amplification techniques for the detection of SARS-CoV-2 and other infectious viruses

As the COVID-19 pandemic continues to affect human health across the globe rapid, simple, point-of-care (POC) diagnosis of infectious viruses such as SARS-CoV-2 remains challenging. Polymerase chain reaction (PCR)-based diagnosis has risen to meet these demands and despite its high-throughput and accuracy, it has failed to gain traction in the rapid, low-cost, point-of-test settings. In contrast, different emerging isothermal amplification-based detection methods show promise in the rapid point-of-test market. In this comprehensive study of the literature, several promising isothermal amplification methods for the detection of SARS-CoV-2 are critically reviewed that can also be applied to other infectious viruses detection. Starting with a brief discussion on the SARS-CoV-2 structure, its genomic features, and the epidemiology of the current pandemic, this review focuses on different emerging isothermal methods and their advancement. The potential of isothermal amplification combined with the revolutionary CRISPR/Cas system for a more powerful detection tool is also critically reviewed. Additionally, the commercial success of several isothermal methods in the pandemic are highlighted. Different variants of SARS-CoV-2 and their implication on isothermal amplifications are also discussed. Furthermore, three most crucial aspects in achieving a simple, fast, and multiplexable platform are addressed.

Microfluidics Technology in SARS-CoV-2 Diagnosis and Beyond: A Systematic Review

With the progression of the COVID-19 pandemic, new technologies are being implemented for more rapid, scalable, and sensitive diagnostics. The implementation of microfluidic techniques and their amalgamation with different detection techniques has led to innovative diagnostics kits to detect SARS-CoV-2 antibodies, antigens, and nucleic acids. In this review, we explore the different microfluidic-based diagnostics kits and how their amalgamation with the various detection techniques has spearheaded their availability throughout the world. Three other online databases, PubMed, ScienceDirect, and Google Scholar, were referred for articles. One thousand one hundred sixty-four articles were determined with the search algorithm of microfluidics followed by diagnostics and SARS-CoV-2. We found that most of the materials used to produce microfluidics devices were the polymer materials such as PDMS, PMMA, and others. Centrifugal force is the most commonly used fluid manipulation technique, followed by electrochemical pumping, capillary action, and isotachophoresis. The implementation of the detection technique varied. In the case of antibody detection, spectrometer-based detection was most common, followed by fluorescence-based as well as colorimetry-based. In contrast, antigen detection implemented electrochemical-based detection followed by fluorescence-based detection, and spectrometer-based detection were most common. Finally, nucleic acid detection exclusively implements fluorescence-based detection with a few colorimetry-based detections. It has been further observed that the sensitivity and specificity of most devices varied with implementing the detection-based technique alongside the fluid manipulation technique. Most microfluidics devices are simple and incorporate the detection-based system within the device. This simplifies the deployment of such devices in a wide range of environments. They can play a significant role in increasing the rate of infection detection and facilitating better health services.

Evaluation of efficiency and sensitivity of 1D and 2D sample pooling strategies for SARS-CoV-2 RT-qPCR screening purposes

To increase the throughput, lower the cost, and save scarce test reagents, laboratories can pool patient samples before SARS-CoV-2 RT-qPCR testing. While different sample pooling methods have been proposed and effectively implemented in some laboratories, no systematic and large-scale evaluations exist using real-life quantitative data gathered throughout the different epidemiological stages. Here, we use anonymous data from 9673 positive cases to model, simulate and compare 1D and 2D pooling strategies. We show that the optimal choice of pooling method and pool size is an intricate decision with a testing population-dependent efficiency-sensitivity trade-off and present an online tool to provide the reader with custom real-time 1D pooling strategy recommendations.

DeepSARS: simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2

BACKGROUND

The continued spread of SARS-CoV-2 and emergence of new variants with higher transmission rates and/or partial resistance to vaccines has further highlighted the need for large-scale testing and genomic surveillance. However, current diagnostic testing (e.g., PCR) and genomic surveillance methods (e.g., whole genome sequencing) are performed separately, thus limiting the detection and tracing of SARS-CoV-2 and emerging variants.

RESULTS

Here, we developed DeepSARS, a high-throughput platform for simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2 by the integration of molecular barcoding, targeted deep sequencing, and computational phylogenetics. DeepSARS enables highly sensitive viral detection, while also capturing genomic diversity and viral evolution. We show that DeepSARS can be rapidly adapted for identification of emerging variants, such as alpha, beta, gamma, and delta strains, and profile mutational changes at the population level.

CONCLUSIONS

DeepSARS sets the foundation for quantitative diagnostics that capture viral evolution and diversity. DeepSARS uses molecular barcodes (BCs) and multiplexed targeted deep sequencing (NGS) to enable simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2. Image was created using Biorender.com .

Group testing via hypergraph factorization applied to COVID-19

Large scale screening is a critical tool in the life sciences, but is often limited by reagents, samples, or cost. An important recent example is the challenge of achieving widespread COVID-19 testing in the face of substantial resource constraints. To tackle this challenge, screening methods must efficiently use testing resources. However, given the global nature of the pandemic, they must also be simple (to aid implementation) and flexible (to be tailored for each setting). Here we propose HYPER, a group testing method based on hypergraph factorization. We provide theoretical characterizations under a general statistical model, and carefully evaluate HYPER with alternatives proposed for COVID-19 under realistic simulations of epidemic spread and viral kinetics. We find that HYPER matches or outperforms the alternatives across a broad range of testing-constrained environments, while also being simpler and more flexible. We provide an online tool to aid lab implementation: http://hyper.covid19-analysis.org .

SARS-CoV-2 Diagnostics Based on Nucleic Acids Amplification: From Fundamental Concepts to Applications and Beyond

COVID-19 pandemic ignited the development of countless molecular methods for the diagnosis of SARS-CoV-2 based either on nucleic acid, or protein analysis, with the first establishing as the most used for routine diagnosis. The methods trusted for day to day analysis of nucleic acids rely on amplification, in order to enable specific SARS-CoV-2 RNA detection. This review aims to compile the state-of-the-art in the field of nucleic acid amplification tests (NAATs) used for SARS-CoV-2 detection, either at the clinic level, or at the Point-Of-Care (POC), thus focusing on isothermal and non-isothermal amplification-based diagnostics, while looking carefully at the concerning virology aspects, steps and instruments a test can involve. Following a theme contextualization in introduction, topics about fundamental knowledge on underlying virology aspects, collection and processing of clinical samples pave the way for a detailed assessment of the amplification and detection technologies. In order to address such themes, nucleic acid amplification methods, the different types of molecular reactions used for DNA detection, as well as the instruments requested for executing such routes of analysis are discussed in the subsequent sections. The benchmark of paradigmatic commercial tests further contributes toward discussion, building on technical aspects addressed in the previous sections and other additional information supplied in that part. The last lines are reserved for looking ahead to the future of NAATs and its importance in tackling this pandemic and other identical upcoming challenges.

Diagnostic performance of RT-PCR-based sample pooling strategy for the detection of SARS-CoV-2

BACKGROUND

The rapid spread of SARS-CoV-2 has created a shortage of supplies of reagents for its detection throughout the world, especially in Latin America. The pooling of samples consists of combining individual patient samples in a block and analyzing the group as a particular sample. This strategy has been shown to reduce the burden of laboratory material and logistical resources by up to 80%. Therefore, we aimed to evaluate the diagnostic performance of the pool of samples analyzed by RT-PCR to detect SARS-CoV-2.

METHODS

A cross-sectional study of diagnostic tests was carried out. We individually evaluated 420 samples, and 42 clusters were formed, each one with ten samples. These clusters could contain 0, 1 or 2 positive samples to simulate a positivity of 0, 10 and 20%, respectively. RT-PCR analyzed the groups for the detection of SARS-CoV-2. The area under the ROC curve (AUC), the Youden index, the global and subgroup sensitivity and specificity were calculated according to their Ct values that were classified as high (H: ≤ 25), moderate (M: 26-30) and low (L: 31-35) concentration of viral RNA.

RESULTS

From a total of 42 pools, 41 (97.6%) obtained the same result as the samples they contained (positive or negative). The AUC for pooling, Youden index, sensitivity, and specificity were 0.98 (95% CI, 0.95-1); 0.97 (95% CI, 0.90-1.03); 96.67% (95% CI; 88.58-100%) and 100% (95% CI; 95.83-100%) respectively. In the stratified analysis of the pools containing samples with Ct ≤ 25, the sensitivity was 100% (95% CI; 90-100%), while with the pools containing samples with Ct ≥ 31, the sensitivity was 80% (95% CI, 34.94-100%). Finally, a higher median was observed in the Ct of the clusters, with respect to the individual samples (p < 0.001).

CONCLUSIONS

The strategy of pooling nasopharyngeal swab samples for analysis by SARS-CoV-2 RT-PCR showed high diagnostic performance.

Assessment of the feasibility of pool testing for SARS-CoV-2 infection screening

BACKGROUND

SARS-CoV-2 pandemic represented a huge challenge for national health systems worldwide. Pooling nasopharyngeal (NP) swabs seems to be a promising strategy, saving time and resources, but it could reduce the sensitivity of the RT-PCR and exacerbate samples management in terms of automation and tracing. In this study, taking advantage of the routine implementation of a screening plan on health workers, we evaluated the feasibility of pool testing for SARS-CoV-2 infection diagnosis in the presence of low viral load samples.

METHOD

Pools were prepared with an automated instrument, mixing 4, 6 or 20 NP specimens, including one, two or none positive samples. Ct values of positive samples were on average about 35 for the four genes analyzed.

RESULTS

The overall sensitivity of 4-samples and 6-samples pools was 93.1 and 90.0%, respectively. Focussing on pools including one sample with Ct value ≥35 for all analyzed genes, sensitivity decreased to 77.8 and 75.0% for 4- and 6-samples, respectively; pools including two positive samples, resulted positive in any size as well as pools including positive samples with Ct values <35.

CONCLUSION

Pool testing strategy should account the balance between cost-effectiveness, dilution effect and prevalence of the infection. Our study demonstrated the good performances in terms of sensitivity and saving resources of pool testing mixing 4 or 6 samples, even including low viral load specimens, in a real screening context possibly affected by prevalence fluctuation. In conclusion, pool testing strategy represents an efficient and resources saving surveillance and tracing tool, especially in specific context like schools, even for monitoring changes in prevalence associated to vaccination campaign.

Importance of sample input volume for accurate SARS-CoV-2 qPCR testing

Nucleic acid testing is the most widely used detection method for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus disease 2019 (COVID-19) pandemic. Currently, a number of COVID-19 real-time quantitative reverse transcription PCR (qPCR) kits with high sensitivity and specificity are available for SARS-CoV-2 testing. However, these qPCR assays are not always reliable in detecting low viral load samples (Ct-value ≥ 35), resulting in inconclusive or false-negative results. Here, we used a Poisson distribution to illustrate the inconsistent performance of qPCR tests in detecting low viral load samples. From this, we concluded that the false-negative outcomes resulted from the random occurrences of sampling zero target molecules in a single test, and the probability to sample zero target molecules in one test decreased significantly with increasing purified RNA or initial sample input volume. At a given RNA concentration of 0.5 copy/μL, the probability of sampling zero RNA molecules decreased from 36.79% to close to 0.67% after increasing the RNA input volume from 2 to 10 μL. A SARS-CoV-2 qPCR assay with an LOD of 300 copies/mL was used to validate the improved consistency of the qPCR tests. We found that the false-negative qPCR results of clinical COVID-19 samples with a Ct ≥ 35 decreased by 50% after increasing the input of purified RNA from 2 to 10 μL. The consistency, accuracy, and robustness of nucleic acid testing for SARS-CoV-2 samples with low viral loads can be improved by increasing the sample input volume.

Constructions and Comparisons of Pooling Matrices for Pooled Testing of COVID-19

In comparison with individual testing, group testing is more efficient in reducing the number of tests and potentially leading to tremendous cost reduction. There are two key elements in a group testing technique: (i) the pooling matrix that directs samples to be pooled into groups, and (ii) the decoding algorithm that uses the group test results to reconstruct the status of each sample. In this paper, we propose a new family of pooling matrices from packing the pencil of lines (PPoL) in a finite projective plane. We compare their performance with various pooling matrices proposed in the literature, including 2D-pooling, P-BEST, and Tapestry, using the two-stage definite defectives (DD) decoding algorithm. By conducting extensive simulations for a range of prevalence rates up to 5%, our numerical results show that there is no pooling matrix with the lowest relative cost in the whole range of the prevalence rates. To optimize the performance, one should choose the right pooling matrix, depending on the prevalence rate. The family of PPoL matrices can dynamically adjust their construction parameters according to the prevalence rates and could be a better alternative than using a fixed pooling matrix.

Identification-detection group testing protocols for COVID-19 at high prevalence

Group testing allows saving chemical reagents, analysis time, and costs, by testing pools of samples instead of individual samples. We introduce a class of group testing protocols with small dilution, suited to operate even at high prevalence (5–10$$\%$$ % ), and maximizing the fraction of samples classified positive/negative within the first round of tests. Precisely, if the tested group has exactly one positive sample then the protocols identify it without further individual tests. The protocols also detect the presence of two or more positives in the group, in which case a second round could be applied to identify the positive individuals. With a prevalence of $$5\%$$ 5 % and maximum dilution 6, with 100 tests we classify 242 individuals, $$92\%$$ 92 % of them in one round and $$8\%$$ 8 % requiring a second individual test. In comparison, the Dorfman’s scheme can test 229 individuals with 100 tests, with a second round for $$18.5\%$$ 18.5 % of the individuals.

Unique Barcoded Primer-Assisted Sample-Specific Pooled Testing (Uni-Pool) for Large-Scale Screening of Viral Pathogens

Pooled testing has been widely adopted recently to facilitate large-scale community testing during the COVID-19 pandemic. This strategy allows to collect and screen multiple specimen samples in a single test, thus immensely saving the assay time and consumable expenses. Nevertheless, when the outcome of a pooled testing is positive, it necessitates repetitive retesting steps for each sample which can pose a serious challenge during a rising infection wave of increasing prevalence. In this work, we develop a unique barcoded primer-assisted sample-specific pooled testing strategy (Uni-Pool) where the key genetic sequences of the viral pathogen in a crude sample are extracted and amplified with concurrent tagging of sample-specific identifiers. This new process improves the existing pooled testing by eliminating the need for retesting and allowing the test results-positive or negative-for all samples in the pool to be revealed by multiplex melting curve analysis right after real-time polymerase chain reaction. It significantly reduces the total assay time for large-scale screening without compromising the specificity and detection sensitivity caused by the sample dilution of pooling. Our method was able to successfully differentiate five samples, positive and negative, in one pool with negligible cross-reactivity among the positive and negative samples. A pooling of 40 simulated samples containing severe acute respiratory syndrome coronavirus-2 pseudovirus of different loads (min: 10 copies/μL; max: 10³ copies/μL) spiked into artificial saliva was demonstrated in eight randomized pools. The outcome of five samples in one pool with a hypothetical infection prevalence of 15% in 40 samples was successfully tested and validated by a typical Dorman-based pooling.

Optical imaging spectroscopy for rapid, primary screening of SARS-CoV-2: a proof of concept

Effective testing is essential to control the coronavirus disease 2019 (COVID-19) transmission. Here we report a-proof-of-concept study on hyperspectral image analysis in the visible and near-infrared range for primary screening at the point-of-care of SARS-CoV-2. We apply spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence to extract information from optical diffuse reflectance measurements from 5 µL fluid samples at pixel, droplet, and patient levels. We discern preparations of engineered lentiviral particles pseudotyped with the spike protein of the SARS-CoV-2 from those with the G protein of the vesicular stomatitis virus in saline solution and artificial saliva. We report a quantitative analysis of 72 samples of nasopharyngeal exudate in a range of SARS-CoV-2 viral loads, and a descriptive study of another 32 fresh human saliva samples. Sensitivity for classification of exudates was 100% with peak specificity of 87.5% for discernment from PCR-negative but symptomatic cases. Proposed technology is reagent-free, fast, and scalable, and could substantially reduce the number of molecular tests currently required for COVID-19 mass screening strategies even in resource-limited settings.

Capacitive Aptasensor Coupled with Microfluidic Enrichment for Real-Time Detection of Trace SARS-CoV-2 Nucleocapsid Protein

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has lasted for almost 2 years. Stemming its spread has posed severe challenges for clinical virus detection. A long turnaround time, complicated operation, and low accuracy have become bottlenecks in developing detection techniques. Adopting a direct antigen detection strategy, we developed a fast-responding and quantitative capacitive aptasensor for ultratrace nucleocapsid protein detection based on a low-cost microelectrode array (MEA) chip. Employing the solid-liquid interface capacitance with a sensitivity of picofarad level, the tiny change on the MEA surface can be definitively detected. As a result, the limit of detection reaches an ultralow level of femtogram per milliliter in different matrices. Integrated with efficient microfluidic enrichment, the response time of this sensor from the sample to the result is shortened to 15 s, completely meeting the real-time detection demand. Moreover, the wide linear range of the sensor is from 10-5 to 10-2 ng/mL, and a high selectivity of 6369:1 is achieved. After application and evaluation in different environmental and body fluid matrices, this sensor and the detection method have proved to be a label-free, real-time, easy-to-operate, and specific strategy for SARS-CoV-2 screening and diagnosis.

Towards an in-situ non-lethal rapid test to accurately detect the presence of the nematode parasite, Anguillicoloides crassus, in European eel, Anguilla anguilla

Anguillicoloides crassus is an invasive nematode parasite of the critically endangered European eel, Anguilla anguilla, and possibly one of the primary drivers of eel population collapse, impacting many features of eel physiology and life history. Early detection of the parasite is vital to limit the spread of A. crassus, to assess its potential impact on spawning biomass. However accurate diagnosis of infection could only be achieved via necropsy. To support eel fisheries management we developed a rapid, non-lethal, minimally invasive and in situ DNA-based method to infer the presence of the parasite in the swim bladder. Screening of 131 wild eels was undertaken between 2017 and 2019 in Ireland and UK to validate the procedure. DNA extractions and PCR were conducted using both a Qiagen Stool kit and in situ using Whatman qualitative filter paper No1 and a miniPCR DNA Discovery-System™. Primers were specifically designed to target the cytochrome oxidase mtDNA gene region and in situ extraction and amplification takes approximately 3 h for up to 16 individuals. Our in-situ diagnostic procedure demonstrated positive predictive values at 96% and negative predictive values at 87% by comparison to necropsy data. Our method could be a valuable tool in the hands of fisheries managers to enable infection control and help protect this iconic but critically endangered species.

Pooled testing efficiency increases with test frequency

Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around [Formula: see text] However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic.

Advanced high-throughput biosensor-based diagnostic approaches for detection of severe acute respiratory syndrome-coronavirus-2

Given the global Coronavirus disease-2019 (COVID-19) pandemic, the transmission, and mortality rate increased drastically and affected the healthcare, financial sectors, and livelihood of the common man. The use of conventional diagnostic tools like reverse transcription-polymerase chain reaction enabled to screen and detecting the spread at a normal pace that had few limitations embedded into their operation, such as complex operation, slow response time, inaccurate results, single laboratory-based operation, and limited sample processing capacity. Consequently, the biosensors have merits that helped in point of care testing, rapid response, simple operation, and multiplex detection among others. Moreover, other advancements in diagnostic tools provided the ability of multiplexing and multioperation attributes for the detection of severe acute respiratory syndrome-Coronavirus-2 that enabled the high-throughput diagnosis of the viral infection in real samples with faster and accurate results. Further, modifications in their methodology, design and detection strategy facilitated their high-throughput property to help in the effective management of the COVID-19 pandemic.

BAYESIAN GROUP TESTING WITH DILUTION EFFECTS

A Bayesian framework for group testing under dilution effects has been developed, using lattice-based models. This work has particular relevance given the pressing public health need to enhance testing capacity for COVID-19 and future pandemics, and the need for wide-scale and repeated testing for surveillance under constantly varying conditions. The proposed Bayesian approach allows for dilution effects in group testing and for general test response distributions beyond just binary outcomes. It is shown that even under strong dilution effects, an intuitive group testing selection rule that relies on the model order structure, referred to as the Bayesian halving algorithm, has attractive optimal convergence properties. Analogous look-ahead rules that can reduce the number of stages in classification by selecting several pooled tests at a time are proposed and evaluated as well. Group testing is demonstrated to provide great savings over individual testing in the number of tests needed, even for moderately high prevalence levels. However, there is a trade-off with higher number of testing stages, and increased variability. A web-based calculator is introduced to assist in weighing these factors and to guide decisions on when and how to pool under various conditions. High performance distributed computing methods have also been implemented for considering larger pool sizes, when savings from group testing can be even more dramatic.

Pooling for SARS-CoV2 Surveillance: Validation and Strategy for Implementation in K-12 Schools

Repeated testing of a population is critical for limiting the spread of the SARS-CoV-2 virus and for the safe reopening of educational institutions such as kindergarten-grade 12 (K-12) schools and colleges. Many screening efforts utilize the CDC RT-PCR based assay which targets two regions of the novel Coronavirus nucleocapsid gene. The standard approach of testing each person individually, however, poses a financial burden to these institutions and is therefore a barrier to using testing for re-opening. Pooling samples from multiple individuals into a single test is an attractive alternate approach that promises significant cost savings-however the specificity and sensitivity of such approaches needs to be assessed prior to deployment. To this end, we conducted a pilot study to evaluate the feasibility of analyzing samples in pools of eight by the established RT-PCR assay. Participants (1,576) were recruited from amongst the Tufts University community undergoing regular screening. Each volunteer provided two swabs, one analyzed separately and the other in a pool of eight. Because the positivity rate was very low, we spiked approximately half of the pools with laboratory-generated swabs produced from known positive cases outside the Tufts testing program. The results of pooled tests had 100% correspondence with those of their respective individual tests. We conclude that pooling eight samples does not negatively impact the specificity or sensitivity of the RT-PCR assay and suggest that this approach can be utilized by institutions seeking to reduce surveillance costs.

Safe and effective pool testing for SARS-CoV-2 detection

Objectives

The global spread of SARS-CoV-2 is a serious public health issue. Large-scale surveillance screenings are crucial but can exceed test capacities. We (A) optimized test conditions and (B) implemented pool testing of respiratory swabs into SARS-CoV-2 diagnostics.

Study design

(A) We determined the optimal pooling strategy and pool size. In addition, we measured the impact of vortexing prior to sample processing, compared a pipette-pooling method (by combining transport medium of several specimens) and a swab-pooling method (by combining several swabs into a test tube filled with PBS) as well as determined the sensitivities of three PCR assays. (B) Finally, we applied high-throughput pool testing for diagnostics.

Results

(A) In a low prevalence setting, we defined a preferable pool size of ten in a two-stage hierarchical pool testing strategy. Vortexing of swabs (n = 33) increased cellular yield by a factor of 2.34. By comparing Ct-values of 16 pools generated with two different pooling strategies, pipette-pooling was more efficient compared to swab-pooling. Measuring dilution series of 20 SARS-CoV-2 positive samples in three PCR assays simultaneously revealed detection rates of 85% (assay I), 50% (assay II), and 95% (assay III) at a 1:100 dilution. (B) We systematically pooled 55,690 samples in a period of 44 weeks resulting in a reduction of 47,369 PCR reactions.

Conclusions

For implementing pooling strategies into high-throughput diagnostics, we recommend utilizing a pipette-pooling method, performing sensitivity validation of the PCR assays used, and vortexing swabs prior to analyses. Pool testing for SARS-CoV-2 detection is feasible and effective in a low prevalence setting.

Optimizing and evaluating PCR-based pooled screening during COVID-19 pandemics

Population screening played a substantial role in safely reopening the economy and avoiding new outbreaks of COVID-19. PCR-based pooled screening makes it possible to test the population with limited resources by pooling multiple individual samples. Our study compared different population-wide screening methods as transmission-mitigating interventions, including pooled PCR, individual PCR, and antigen screening. Incorporating testing-isolation process and individual-level viral load trajectories into an epidemic model, we further studied the impacts of testing-isolation on test sensitivities. Results show that the testing-isolation process could maintain a stable test sensitivity during the outbreak by removing most infected individuals, especially during the epidemic decline. Moreover, we compared the efficiency, accuracy, and cost of different screening methods during the pandemic. Our results show that PCR-based pooled screening is cost-effective in reversing the pandemic at low prevalence. When the prevalence is high, PCR-based pooled screening may not stop the outbreak. In contrast, antigen screening with sufficient frequency could reverse the epidemic, despite the high cost and the large numbers of false positives in the screening process.

An accurate model for SARS-CoV-2 pooled RT-PCR test errors

Pooling is a method of simultaneously testing multiple samples for the presence of pathogens. Pooling of SARS-CoV-2 tests is increasing in popularity, due to its high testing throughput. A popular pooling scheme is Dorfman pooling: test N individuals simultaneously, if the test is positive, each individual is then tested separately; otherwise, all are declared negative. Most analyses of the error rates of pooling schemes assume that including more than a single infected sample in a pooled test does not increase the probability of a positive outcome. We challenge this assumption with experimental data and suggest a novel and parsimonious probabilistic model for the outcomes of pooled tests. As an application, we analyse the false-negative rate (i.e. the probability of a negative result for an infected individual) of Dorfman pooling. We show that the false-negative rates under Dorfman pooling increase when the prevalence of infection decreases. However, low infection prevalence is exactly the condition when Dorfman pooling achieves highest throughput efficiency. We therefore urge the cautious use of pooling and development of pooling schemes that consider correctly accounting for tests' error rates.

Improvement of Sensitivity of Pooling Strategies for COVID-19

Group testing (or pool testing), for example, Dorfman's method or grid method, has been validated for COVID-19 RT-PCR tests and implemented widely by most laboratories in many countries. These methods take advantages since they reduce resources, time, and overall costs required for a large number of samples. However, these methods could have more false negative cases and lower sensitivity. In order to maintain both accuracy and efficiency for different prevalence, we provide a novel pooling strategy based on the grid method with an extra pool set and an optimized rule inspired by the idea of error-correcting codes. The mathematical analysis shows that (i) the proposed method has the best sensitivity among all the methods we compared, if the false negative rate (FNR) of an individual test is in the range [1%, 20%] and the FNR of a pool test is closed to that of an individual test, and (ii) the proposed method is efficient when the prevalence is below 10%. Numerical simulations are also performed to confirm the theoretical derivations. In summary, the proposed method is shown to be felicitous under the above conditions in the epidemic.

Rapid design and implementation of an adaptive pooling workflow for SARS-CoV-2 testing in an NHS diagnostic laboratory: a proof-of-concept study

Background: Diagnostic laboratories are currently required to provide routine testing of asymptomatic staff and patients as a part of their clinical screening for SARS-CoV-2 infection. However, these cohorts display very different disease prevalence from symptomatic individuals and testing capacity for asymptomatic screening is often limited. Group testing is frequently proposed as a possible solution to address this; however, proposals neglect the technical and operational feasibility of implementation in a front-line diagnostic laboratory. Methods: Between October and December 2020, as a seven-week proof of concept, we took into account scientific, technical and operational feasibility to design and implement an adaptive pooling strategy in an NHS diagnostic laboratory in London (UK). We assessed the impact of pooling on analytical sensitivity and modelled the impact of prevalence on pooling strategy. We then considered the operational constraints to model the potential gains in capacity and the requirements for additional staff and infrastructure. Finally, we developed a LIMS-agnostic laboratory automation workflow and software solution and tested the technical feasibility of our adaptive pooling workflow. Results: First, we determined the analytical sensitivity of the implemented SARS-CoV-2 assay to be 250 copies/mL. We then determined that, in a setting with limited analyser capacity, the testing capacity could be increased by two-fold with pooling, however, in a setting with limited reagents, this could rise to a five-fold increase. These capacity increases could be realized with modest additional resource and staffing requirements whilst utilizing up to 76% fewer plastic consumables and 90% fewer reagents. Finally, we successfully implemented a plate-based pooling workflow and tested 920 patient samples using the reagents that would usually be required to process just 222 samples. Conclusions: Adaptive pooled testing is a scientifically, technically and operationally feasible solution to increase testing capacity in frontline NHS diagnostic laboratories.

Evaluation of the SARS-CoV-2 Inactivation Efficacy Associated With Buffers From Three Kits Used on High-Throughput RNA Extraction Platforms

Rapid and demonstrable inactivation of SARS-CoV-2 is crucial to ensure operator safety during high-throughput testing of clinical samples. The inactivation efficacy of SARS-CoV-2 was evaluated using commercially available lysis buffers from three viral RNA extraction kits used on two high-throughput (96-well) RNA extraction platforms (Qiagen QIAcube HT and the Thermo Fisher KingFisher Flex) in combination with thermal treatment. Buffer volumes and sample ratios were chosen for their optimised suitability for RNA extraction rather than inactivation efficacy and tested against a representative sample type: SARS-CoV-2 spiked into viral transport medium (VTM). A lysis buffer mix from the MagMAX Pathogen RNA/DNA kit (Thermo Fisher), used on the KingFisher Flex, which included guanidinium isothiocyanate (GITC), a detergent, and isopropanol, demonstrated a minimum inactivation efficacy of 1 × 10⁵ tissue culture infectious dose (TCID)₅₀/ml. Alternative lysis buffer mixes from the MagMAX Viral/Pathogen Nucleic Acid kit (Thermo Fisher) also used on the KingFisher Flex and from the QIAamp 96 Virus QIAcube HT Kit (Qiagen) used on the QIAcube HT (both of which contained GITC and a detergent) reduced titres by 1 × 10⁴ TCID₅₀/ml but did not completely inactivate the virus. Heat treatment alone (15 min, 68°C) did not completely inactivate the virus, demonstrating a reduction of 1 × 10³ TCID₅₀/ml. When inactivation methods included both heat treatment and addition of lysis buffer, all methods were shown to completely inactivate SARS-CoV-2 inactivation against the viral titres tested. Results are discussed in the context of the operation of a high-throughput diagnostic laboratory.

Pooled Testing Strategies for SARS-CoV-2 diagnosis: A comprehensive review

SARS-CoV-2 has surged across the globe causing the ongoing COVID-19 pandemic. Systematic testing to facilitate index case isolation and contact tracing is needed for efficient containment of viral spread. The major bottleneck in leveraging testing capacity has been the lack of diagnostic resources. Pooled testing is a potential approach that could reduce cost and usage of test kits. This method involves pooling individual samples and testing them 'en bloc'. Only if the pool tests positive, retesting of individual samples is performed. Upon reviewing recent articles on this strategy employed in various SARS-CoV-2 testing scenarios, we found substantial diversity emphasizing the requirement of a common protocol. In this article, we review various theoretically simulated and clinically validated pooled testing models and propose practical guidelines on applying this strategy for large scale screening. If implemented properly, the proposed approach could contribute to proper utilization of testing resources and flattening of infection curve.

Prospective evaluation of specimen pooling strategy for detection of SARS-CoV-2 using pools of five and six specimens

The increased demand for SARS-CoV-2 molecular testing during the COVID-19 pandemic resulted in shortage of reagents and consumables. Pooling of specimens could be an alternative strategy to overcome these problems. Initial evaluation of the pooling strategy was performed using known positive specimens, previously tested individually, and their respective pools of plus four (5X), five (6X) and nine (10X) known negative specimens. Subsequently, 35 positive 5X and 35 positive 6X pools containing only one positive specimen per pool were analyzed prospectively regarding the difference in Ct values in pooled versus individual specimens. When the number of samples in the pool were five or six, the average deviation of Ct differences was < 1; therefore, this strategy was followed in the prospective study. Significant difference in Ct values was observed in positive specimens when tested individually and in 5X pools (p = 0.006), while the difference was not significant when positive specimens were tested individually and in 6X pools (p = 0.07). The difference in Ct values was not significant between the 5X and 6X pools. Testing in pools of five or six specimens is a reliable option for SARS-CoV-2 RNA detection when mass testing is needed.

Nested pool testing strategy for the diagnosis of infectious diseases

The progress of the SARS-CoV-2 pandemic requires the design of large-scale, cost-effective testing programs. Pooling samples provides a solution if the tests are sensitive enough. In this regard, the use of the gold standard, RT-qPCR, raises some concerns. Recently, droplet digital PCR (ddPCR) was shown to be 10-100 times more sensitive than RT-qPCR, making it more suitable for pooling. Furthermore, ddPCR quantifies the RNA content directly, a feature that, as we show, can be used to identify nonviable samples in pools. Cost-effective strategies require the definition of efficient deconvolution and re-testing procedures. In this paper we analyze the practical implementation of an efficient hierarchical pooling strategy for which we have recently derived the optimal, determining the best ways to proceed when there are impediments for the use of the absolute optimum or when multiple pools are tested simultaneously and there are restrictions on the throughput time. We also show how the ddPCR RNA quantification and the nested nature of the strategy can be combined to perform self-consistency tests for a better identification of infected individuals and nonviable samples. The studies are useful to those considering pool testing for the identification of infected individuals.