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• W3046557687 abstract "Routine testing for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in health care workers (HCWs) is critical. Group testing strategies to increase capacity facilitate mass population testing but do not prioritize turnaround time, an important consideration for HCW screening. We propose a nonadaptive combinatorial (NAC) group testing strategy to increase throughput while facilitating rapid turnaround. NAC matrices were constructed for sample sizes of 700, 350, and 250. Matrix performance was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1% to 10%. NAC matrices were compared versus Dorfman sequential (DS) group testing approaches. NAC matrices performed well at low prevalence levels, with an average of 97% of samples resolved after a single round of testing via the n = 700 matrix at a prevalence of 1%. In simulations of low to medium (0.1% to 3%) prevalence, all NAC matrices were superior to the DS strategy, measured by fewer repeated tests required. At very high prevalence levels (10%), the DS matrix was marginally superior, although both group testing approaches performed poorly at high prevalence levels. This strategy maximizes the proportion of samples resolved after a single round of testing, allowing prompt return of results to HCWs. This methodology may allow laboratories to adapt their testing scheme based on required throughput and the current population prevalence, facilitating a data-driven testing strategy. Routine testing for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in health care workers (HCWs) is critical. Group testing strategies to increase capacity facilitate mass population testing but do not prioritize turnaround time, an important consideration for HCW screening. We propose a nonadaptive combinatorial (NAC) group testing strategy to increase throughput while facilitating rapid turnaround. NAC matrices were constructed for sample sizes of 700, 350, and 250. Matrix performance was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1% to 10%. NAC matrices were compared versus Dorfman sequential (DS) group testing approaches. NAC matrices performed well at low prevalence levels, with an average of 97% of samples resolved after a single round of testing via the n = 700 matrix at a prevalence of 1%. In simulations of low to medium (0.1% to 3%) prevalence, all NAC matrices were superior to the DS strategy, measured by fewer repeated tests required. At very high prevalence levels (10%), the DS matrix was marginally superior, although both group testing approaches performed poorly at high prevalence levels. This strategy maximizes the proportion of samples resolved after a single round of testing, allowing prompt return of results to HCWs. This methodology may allow laboratories to adapt their testing scheme based on required throughput and the current population prevalence, facilitating a data-driven testing strategy. Throughout the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak, there have been calls for widespread testing to help track and suppress viral transmission.1Black J.R.M. Bailey C. Przewrocka J. Dijkstra K.K. Swanton C. COVID-19: the case for health-care worker screening to prevent hospital transmission.Lancet. 2020; 395: 1418-1420Abstract Full Text Full Text PDF PubMed Scopus (279) Google Scholar,2Peto J. Alwan N.A. Godfrey K.M. Burgess R.A. Hunter D.J. Riboli E. Romer P. signatories: Universal weekly testing as the UK COVID-19 lockdown exit strategy.Lancet. 2020; 395: 1420-1421Abstract Full Text Full Text PDF PubMed Scopus (97) Google Scholar Many countries have adopted high-throughput testing strategies, and tens of millions of SARS-CoV-2 antigen tests have been performed since the outbreak began. The reagents required to perform these tests, because of the unparalleled global demand, are a limited resource, and their utilization should be optimized. Certain settings, such as hospitals and care homes, have the potential to act as persistent reservoirs of infection where the reproduction value remains persistently elevated. Approaches to ameliorate nosocomial spread include access to adequate personal protective equipment, effective cohorting of patients, and the proactive identification of infectious staff members.1Black J.R.M. Bailey C. Przewrocka J. Dijkstra K.K. Swanton C. COVID-19: the case for health-care worker screening to prevent hospital transmission.Lancet. 2020; 395: 1418-1420Abstract Full Text Full Text PDF PubMed Scopus (279) Google Scholar,3McDermott J.H. Stoddard D. Ellingford J.M. Gokhale D. Reynard C. Black G. Body R. Newman W.G. Utilizing point-of-care diagnostics to minimize nosocomial infection in the 2019 novel coronavirus (SARS-CoV-2) pandemic.QJM. 2020; 113: 851-853Crossref PubMed Scopus (4) Google Scholar Staff members who develop symptoms should isolate. However, a yet undetermined proportion of patients infected with SARS-CoV-2 develop an asymptomatic viremia.4Zhang J. Tian S. Lou J. Chen Y. Familial cluster of COVID-19 infection from an asymptomatic.Crit Care. 2020; 24: 119Crossref PubMed Scopus (79) Google Scholar,5Wu Z. McGoogan J.M. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention.JAMA. 2020; 323: 1239-1242Crossref PubMed Scopus (11372) Google Scholar These asymptomatic carriers pose a serious challenge when attempting to prevent spread within hospitals, environments where staff often congregate in close proximity with vulnerable patients. Because of this scenario, demand is growing for the routine testing of health care workers (HCWs), a premise supported by modeling which suggested that screening, irrespective of symptoms, could reduce transmission by 25% to 33%.1Black J.R.M. Bailey C. Przewrocka J. Dijkstra K.K. Swanton C. COVID-19: the case for health-care worker screening to prevent hospital transmission.Lancet. 2020; 395: 1418-1420Abstract Full Text Full Text PDF PubMed Scopus (279) Google Scholar,6Grassly N. Pons-Salort M. Parker E.P. White P.J. Report 16: Role of Testing in COVID-19 Control. Imperial College London.2020Google Scholar One of the main hurdles to initiating a comprehensive hospital staff testing program is the large number of staff requiring testing and the rapid turnaround times that would be required to make any screening strategy useful. An estimated 1.5 million staff work in the National Health Service, with larger hospitals each employing >20,000 employees. The requirement to test even a small proportion of these HCWs would dwarf the UK’s testing capacity and would be prohibitively expensive using standard monoplex testing, even taking into account the estimated £5 billion the UK government plans to spend on SARS-CoV-2 over the next 2 years.7Garside J. UK Set to Award Covid-19 Testing Contracts Worth £5bn to Private Bidders. The Guardian.2020Google Scholar However, in moving from an individual diagnostic approach toward the population-based screening of asymptomatic individuals, there is a key shift in the philosophy underpinning the application of testing, and alternative diagnostic approaches could be used, specifically a group testing strategy. A group testing strategy is where samples taken from more than one individual are tested at the same time. The principle is that if a pool test result returns negative, then everyone’s sample in that pool is negative. If a pool test result returns positive, then at least one sample in that pool is positive (Figure 1). The concept was first introduced by Dorfman in 1943, who proposed it as an approach to screen soldiers for syphilis during World War II.8Dorfman R. The detection of defective members of large populations.Ann Math Stat. 1943; 14: 436-440Crossref Google Scholar Most group testing approaches used since have been based on Dorfman’s original methodology. This involves the pooling of multiple samples and, if a pool test result returns positive, the constituent samples undergo further testing (Figure 1).9Eberhardt J.N. Breuckmann N.P. Eberhardt C.S. Multi-stage group testing improves efficiency of large-scale COVID-19 screening.J Clin Virol. 2020; 128: 104382Crossref PubMed Scopus (62) Google Scholar This approach is henceforth referred to as Dorfman sequential (DS) pooling. Although DS pooling can significantly increase capacity compared with a monoplex approach, throughput may not be maximized because at least two rounds of testing are required to differentiate positive samples within a pool. In the case of SARS-CoV-2, if the samples were pooled before viral RNA extraction, then a further RNA extraction step would be required for the second round of testing, significantly slowing the process.10Sahajpal N.S. Mondal A.K. Njau A. Ananth S. Jones K. Ahluwalia P.K. Ahluwalia M. Jilani Y. Chaubey A. Hegde M. Kota V. Rojiani A. Kolhe R. Proposal of RT-PCR-based mass population screening for severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019).J Mol Diagn. 2020; 22: 1294-1299Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar In the context of HCW screening, any lengthening of the testing process could mean a reduced workforce while staff await their results, affecting patient care. Nonadaptive approaches (Figure 1) overcome the requirement for repeated testing rounds by testing the same sample in several simultaneously assayed pools, aiming to maximize the proportion of samples resolved after a single round of testing. Although DS approaches may ultimately require fewer tests over several rounds compared with nonadaptive approaches, in the context of HCW screening, maximizing the number of true-negative results identified after a single round of testing should be the priority for any testing scheme. We contend that multistage group testing approaches are not the most suitable option for HCW screening because they do not recognize the need for rapid results. More than one round of group testing would introduce a complexity into assay design, and results may be considerably delayed for some individuals if their samples are not resolved in the first round. Herein, we suggest that a group testing approach should be used as an initial screen, maximizing the number of true-negative findings identified, before monoplex testing is used to determine any indeterminate samples (Figure 2). We propose a nonadaptive combinatorial (NAC) pooling approach as an alternative screening strategy to maximize throughput after a single round of testing in the context of varying population prevalences of SARS-CoV-2 infection. To establish an approximate suitable limit of detection for pooling, nasopharyngeal samples of known SARS-CoV-2 status were pooled before extraction. Two pools (Pool 1 and Pool 2) were prepared, each comprising 14 SARS-CoV-2–negative samples and one SARS-CoV-2–positive sample. Pool 1 contained a positive sample with a viral load near the limit of detection at previous testing using the N-gene assay [cycle threshold = 37; Integrated DNA Technologies (IDT), San Diego, CA]. Pool 2 contained a positive sample with a mid-level viral load (cycle threshold = 30, classified as mid-range from >100 positive clinical samples). The IDT N-Gene Emergency Use Authorization assay comprises two targets located in the CV19 N-gene (N1 and N2). The assay has been designed and batch verified by the CDC and is run according to CDC protocol.11Centers for Disease Control and PreventionCDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel.2020Google Scholar Pooled samples were extracted by using the AmoyDx Virus/Cell RNA kit (AmoyDx, Xiamen, China). RNA was tested for SARS-CoV-2 by using the IDT N-gene Emergency Use Authorization assay and run on the ABI QuantStudio 6 instrument (Thermo Fisher Scientific, Waltham, MA); 200 copies of 2019-nCoV_N_Positive (IDT) were run as a positive control. Baselines and thresholds were defined automatically by the ABI QuantStudio 6 software. SARS-CoV-2–positive or –negative status was assigned by using CDC-defined criteria (Supplemental Table S1). Simulations were run by using the R software package (R Foundation for Statistical Computing, Vienna, Austria) to construct NAC pooling matrices for different predetermined input values of n (the number of samples) and w (the number of wells to which each sample is allocated). The number of wells is assumed to be fixed at 96 throughout, and the maximum pool size was fixed at 15, informed by the limit of detection study as described in the prior paragraph. Example pooling matrices were generated for n values of 700, 350, and 250, with w values of 2, 4, and 5, respectively. The code for the matrix design algorithm is publicly accessible (GitHub, https://github.com/duncstod/grouptesting, last accessed January 17, 2021). Each pooling matrix was constructed by randomly allocating samples to wells until the following conditions are satisfied: no sample is tested in the same well more than once, and no sample pairs are tested together in the same wells more than once. The maximum number of guaranteed positive findings that a pooling matrix can identify is r-1. Such matrices are r-1 disjunct. Pooling matrices can be accessed at www.samplepooling.com (last accessed January 17, 2021). The performance of each nonadaptive matrix was tested against a matrix with a w value of 1 at the same sample size. This w = 1 matrix represents the first stage of a DS testing scheme and is henceforth referred to as the DS matrix. Pool size for the DS matrices was determined by the number of samples divided by the number of wells, and each sample was allocated to a single well. The efficacy of each matrix was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1%, 1%, 3%, 7%, and 10% (Table 1). Each simulation ran for 10,000 iterations. In each iteration, every sample is designated as positive or negative by n draws from a Bernoulli distribution with p = 0.1%, 1%, 3%, 7%, and 10%.Table 1An Outline of the Three Different Nonadaptive Combinatorial Matrices Tested at Five Different Population Prevalence LevelsSimulation numbernwp, %Tests confirmed negative, nTrue negatives confirmed, %Expected retests required, nExpected tests saved, n170020.16999916032168197185863360186100504473935630529951026438435169635040.13501000254713469942508333596152399727579751791010209601411131125050.1250100015412124799315113324297814614721787331211510182726985Figures are presented as mean values from 10,000 simulations.n, sample size; p, population prevalence; w, number of times the sample is repeated over the assay plate. Open table in a new tab Figures are presented as mean values from 10,000 simulations. n, sample size; p, population prevalence; w, number of times the sample is repeated over the assay plate. From each simulation, pooling matrix performance statistics were dervied, tested with zero error, including the number of tests saved compared with a monoplex approach, measured as where n represents the total samples tested on a 96-well plate and indeterminate results are those samples that cannot be decoded through the matrix (ie, retesting is required). The average proportion of samples confirmed as negative for each matrix was also determined. A Web platform was designed (www.samplepooling.com) that allows users to choose their matrix (n = 700, n = 350, and n = 250) and decode their results. A combinatorial orthogonal matching pursuit algorithm initially assumes that each sample is positive at the beginning of the decoding process.12Johnson O. Aldridge M. Scarlett J. Performance of group testing algorithms with near-constant tests per item.IEEE Trans Inf Theory. 2019; 65: 707-723Crossref Scopus (35) Google Scholar Attempts are then made to disprove this assertion by finding a well in which the sample has been placed that has been called as negative. A definite defective algorithm was then used to find positive wells, which contain a single sample on the list of potentially positive samples. This attributes a status for each result as either “positive” or “indeterminate result,” in which reanalysis is suggested. If the decoding system identifies a well that is not consistent with the matrix, such as a false-positive result, the results for the concordant wells are displayed but an error message is shown for the discrepant well, and reanalysis is suggested. The code is publicly accessible via GitHub (https://github.com/MCGM-Covid-19/matrix-decoder.github.io, last accessed January 17, 2021). To establish a suitable limit of detection for pooling, nasopharyngeal samples of known SARS-CoV-2 status were placed in two pools, each comprising 14 SARS-CoV-2–negative samples and one SARS-CoV-2–positive sample, with the positive samples at differing viral loads. Both pooled samples tested positive for SARS-Cov-2 under the CDC-defined guidelines; this outcome indicated that positive samples can be detected when diluted 15-fold (Supplemental Table S1). This limit was used as an assumption to inform the design of the pooling matrices. The performance of all the nonadaptive combinational matrices, as defined by the expected number of retests required, deteriorated as the population prevalence increased (Table 1). Matrices that were designed to test more samples were less tolerant to increases in population prevalence than those designed to test fewer samples (Figure 3). In the simulation in which there was a population prevalence of 1%, the n = 700 NAC matrix performed well, with an average of only 19 samples requiring retesting, representing an average of 585 tests saved per 96-well plate. However, as the population prevalence increased, the average number of tests decreased markedly, with an average of only 168 tests saved and 436 retests required when the population prevalence was at 10%. Given that the matrices were tested by using draws from a Bernoulli distribution, there were some simulations in which the actual sample positivity rate was >10%. Here the performance of the n = 700 matrix deteriorated significantly, with some simulations saving almost no tests compared with the no-pooling approach (Figure 3). The other models (n = 350 and n = 250) were notably more tolerant to an increase in population prevalence than the n = 700 model. This is best shown by the proportion of test results confirmed as negative (Figure 4). At a prevalence of 10%, the proportion of true negatives that were confirmed in the n = 700 matrix was 38%. This outcome is compared with the n = 350 and n = 250 models that were able to confirm an average of 60% and 73% as negative, respectively. The n = 250 matrix was most tolerant to an increase in the population prevalence, with most simulations proving relatively robust at a prevalence of 7%, with an average of 87% of test results confirmed negative after a single run. At low population prevalence levels (0.1% to 1%), all NAC matrices had a near perfect performance as measured by the proportion of true-negative findings identified after a single run (Figure 4). The performance of each matrix was compared with that of a DS group testing strategy at the same population prevalence (Figure 5). Both the DS and NAC matrices saw their discriminatory ability deteriorate as the population prevalence increased. The n = 250 and n = 350 matrices were superior to DS approaches at all population prevalence levels except for when the prevalence reached 10% (Figure 5 and Supplemental Figure S1). At a population prevalence of 10%, the DS approach was marginally superior for all matrices, although both group testing strategies at this prevalence level performed poorly, with >350 retests required in both. The n = 700 NAC matrix was the least robust; although it performed better than its DS counterpart at low prevalence levels (0.1% to 3%), its discriminatory ability was inferior once the population prevalence rose to >7% (Figure 4). Since the SARS-CoV-2 outbreak began in late 2019, there have been >70 million reported cases, and few countries remain unaffected. Despite the global nature of the pandemic, the status of the outbreak differs markedly between nations, and the prevalence in each nation is not homogeneous. Given this variation, it is likely that the optimal testing strategy in one country will not be optimal in another country. Similarly, the most appropriate testing approach may change within a single country as the population prevalence changes or if there are certain settings, such as health care facilities, in which the expected positivity rate is higher. An NAC group testing strategy offers a method that can be adapted based on the expected local or national population prevalence, increasing throughput and saving reagents. We designed three NAC matrices and tested their single round efficacy at varying population prevalences. When the prevalence was low, all the matrices performed well, with only fractions of samples requiring retesting. This was especially true in very low prevalence settings (0.1%), in which even in the least tolerant n = 700 matrix, only one retest was required on average. However, as the population prevalence increased, the performance of each matrix deteriorated. Even the most resilient matrix, n = 250, was limited in its discriminatory ability when the population prevalence rose to >10%. Numerous reports early in the pandemic detailed programs designed to offer testing to symptomatic HCWs at hospitals across the UK.13Hunter E. Price D.A. Murphy E. van der Loeff I.S. Baker K.F. Lendrem D. Lendrem C. Schmid M.L. Pareja-Cebrian L. Welch A. Payne B.A.I. Duncan C.J.A. First experience of COVID-19 screening of health-care workers in England.Lancet. 2020; 395: e77-e78Abstract Full Text Full Text PDF PubMed Scopus (203) Google Scholar,14Keeley A.J. Evans C. Colton H. Ankcorn M. Cope A. State A. Bennett T. Giri P. de Silva T.I. Raza M. Roll-out of SARS-CoV-2 testing for healthcare workers at a large NHS Foundation Trust in the United Kingdom, March 2020.Euro Surveill. 2020; 25: 2000433Crossref Scopus (114) Google Scholar In these trials, the positivity rate in symptomatic staff members ranged from 14% to 20%. Any pooling strategy, if the positivity rate was this high, would perform poorly and the number of retests required would become onerous, defeating the objective of a group testing strategy. This is supported by the data presented here, and the use of pooling when the prevalence of SARS-CoV-2 is >7% is not recommended. Rather, the current monoplex approach would likely remain most practical. The data outlined in this work show that pooling becomes increasingly useful as the population prevalence of SARS-CoV-2 decreases. A recent trial at Barts Hospital in London tested asymptomatic HCWs for 5 consecutive weeks, with positivity rates ranging from 7.1% to 1.1%.15Treibel T.A. Manisty C. Burton M. McKnight A. Lambourne J. Augusto J.B. Couto-Parada X. Cutino-Moguel T. Noursadeghi M. Moon J.C. COVID-19: PCR screening of asymptomatic health-care workers at London hospital.Lancet. 2020; 395: 1608-1610Abstract Full Text Full Text PDF PubMed Scopus (209) Google Scholar The peak of 7.1% was recorded on March 30, 2020, one week after the UK-wide lockdown and at a time when community viral transmission was likely to have been at its highest. This changing prevalence shows how a context-specific adaptive testing strategy for staff testing might be deployed. Initially the most conservative matrix, n = 250, should be used to establish the population prevalence. At a positivity rate of 7.1%, this matrix would still be able to identify 90% of true-negative findings save approximately 121 tests relative to a monoplex approach, superior to a DS strategy at the same sample size. If the positivity rate was lower, this would then inform the choice of the matrix for subsequent testing rounds. As the rate falls, the utilization of less tolerant but higher throughput assays could be used, such as the n = 700 matrix described here. A maximum pool size of 15 was a formal condition for all the matrices outlined in this work. Based on previously published work, it is likely that this pool size could be increased without losing assay sensitivity. A recent correspondence outlined how pooling of up to 30 samples could be used to increase throughput without affecting diagnostic accuracy, although it was noted that borderline positive samples may escape detection in larger pools.16Lohse S. Pfuhl T. Berkó-Göttel B. Rissland J. Geißler T. Gärtner B. Becker S.L. Schneitler S. Smola S. Pooling of samples for testing for SARS-CoV-2 in asymptomatic people.Lancet Infect Dis. 2020; 20: 1231-1232Abstract Full Text Full Text PDF PubMed Scopus (215) Google Scholar A pool size of 15 samples was tested and subsequently used for two main reasons. First, this pool size will likely be more sensitive to borderline positive samples than larger pools, an important benefit when considering the importance of avoiding false-negative results in HCWs who interact with vulnerable patients. Second, although higher pool sizes increase the theoretical throughput of a single plate, the complexity of the assay design and processing also increases. Critically, we propose these pooling approaches to improve throughput, save resources, and reduce the time that HCWs are waiting for their results. Typically, group testing strategies use subsequent rounds of group testing. These approaches are likely to be mathematically more efficient, as measured according to the number of tests required to resolve all the samples, than our approach outlined here.17Mallapaty S. The mathematical strategy that could transform coronavirus testing.Nature. 2020; 583: 504-505Crossref PubMed Scopus (41) Google Scholar However, more than one round of group testing introduces complexity, using laboratory resources and increasing the time to results. Any testing scheme will have positive and negative attributes that can be broadly split into throughput, reagent efficiency, speed, and complexity. The most suitable testing strategy will be context dependent. In some situations, such as widespread population screening, throughput will understandably be the dominant attribute and complex, multistage, centralized approaches can be used.18Mutesa L. Ndishimye P. Butera Y. Souopgui J. Uwineza A. Rutayisire R. Musoni E. Rujeni N. Nyatanyi T. Ntagwabira E. Semakula M. Musanabaganwa C. Nyamwasa D. Ndashimye M. Ujeneza E. Mwikarago I.E. Muvunyi C.M. Mazarati J.B. Nsanzimana S. Turok N. Ndifon W. A strategy for finding people infected with SARS-CoV-2: optimizing pooled testing at low prevalence.arXiv. 2020; ([Epub] doi:)10.1101/2020.05.02.20087924Google Scholar In the context of HCW screening, a more subtle balance must be struck between speed and throughput, with complexity reduced if testing is being performed on a more local basis. We believe that the approach described here (an initial NAC screen followed by monoplex testing) provides a relatively high throughout system with good efficiency, and the design can be varied depending on the local sample size and expected population prevalence. Three distinct NAC matrices were created and their performance was tested against other testing approaches. These matrices and the system to decode the results are freely accessible (www.samplepooling.com). At low to medium (0.1% to 3%) positivity rates, as would be expected in asymptomatic HCWs, the matrices are able to increase throughput and reduce the requirement for repeated testing compared with DS or standard monoplex schemes. The benefit of the approach outlined here is that laboratories can choose the matrix which most suits their current population prevalence and sample size, facilitating a context-specific, data-driven testing approach. J.H.M., G.B., D.G., and W.G.N. designed the study; D.S., J.ME., L.A.M.D., and J.H.M. performed the experiments; L.A.M.D. performed the pooling experiments; D.S. developed and tested the matrices; P.J.W. reviewed the performance of the matrices; A.T. developed the freely accessible web application; and J.H.M. wrote the manuscript; all authors reviewed and approved the final manuscript. Download .docx (.01 MB) Help with docx files Supplemental Table S1" @default.
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• W3046557687 title "A Nonadaptive Combinatorial Group Testing Strategy to Facilitate Health Care Worker Screening during the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Outbreak" @default.
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