Assessing the dilution effect of specimen pooling on the sensitivity of SARS-CoV-2 PCR tests

Determining diagnostic sensitivity loss limits for sample pooling in duplex rtPCR surveillance testing: Theileria orientalis and Anaplasma marginale

To expand surveillance testing capacity through sample pooling, a thorough understanding is needed of how sample dilution through pooling affects the sensitivity of candidate assays. We validated a robust and representative framework for assessing the dilution effect of sample pooling using duplex rtPCR surveillance of Theileria orientalis and Anaplasma marginale, both of which are causative agents of severe anemia in cattle and a serious threat to the cattle industry in Virginia and many other states. We used 200 known-positive samples with Ct values representative of typical surveillance results in a series of pools in which we re-tested each sample individually, followed by each sample diluted in equal volumes with negative samples to make pools of 2, 4, 6, 8, and 10 total samples. We compared the Ct values of the individual positives with the Ct values of each pool size to determine if Ct values increase past the limit of detection in the 45-cycle assay. We observed a maximum of 2% sensitivity loss (no more than 2 of 100 samples returned a false-negative result) for both T. orientalis and A. marginale during the pooling series, with lower-than-expected average Ct increase and sensitivity loss. We conclude that pooling up to 10 samples would be acceptable for regional surveillance of T. orientalis and A. marginale using our rtPCR assay. The described strategy is applicable to validate pooling for a wide range of single and duplex rtPCR assays, which could expand efficient disease surveillance.

A proactive/reactive mass screening approach with uncertain symptomatic cases

We study the problem of mass screening of heterogeneous populations under limited testing budget. Mass screening is an essential tool that arises in various settings, e.g., the COVID-19 pandemic. The objective of mass screening is to classify the entire population as positive or negative for a disease as efficiently and accurately as possible. Under limited budget, testing facilities need to allocate a portion of the budget to target sub-populations (i.e., proactive screening) while reserving the remaining budget to screen for symptomatic cases (i.e., reactive screening). This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations for proactive/reactive screening. The framework also incorporates two widely used testing schemes: Individual and Dorfman group testing. By leveraging the special structure of the resulting bilinear optimization problem, we identify key structural properties, which in turn enable us to develop efficient solution schemes. Furthermore, we extend the model to accommodate customized testing schemes across different sub-populations and introduce a highly efficient heuristic solution algorithm for the generalized model. We conduct a comprehensive case study on COVID-19 in the US, utilizing geographically-based data. Numerical results demonstrate a significant improvement of up to 52% in total misclassifications compared to conventional screening strategies. In addition, our case study offers valuable managerial insights regarding the allocation of proactive/reactive measures and budget across diverse geographic regions.

An optimization framework for large-scale screening under limited testing capacity with application to COVID-19

We consider the problem of targeted mass screening of heterogeneous populations under limited testing capacity. Mass screening is an essential tool that arises in various settings, e.g., ensuring a safe supply of blood, reducing prevalence of sexually transmitted diseases, and mitigating the spread of infectious disease outbreaks. The goal of mass screening is to classify whole population groups as positive or negative for an infectious disease as efficiently and accurately as possible. Under limited testing capacity, it is not possible to screen the entire population and hence administrators must reserve testing and target those among the population that are most in need or most susceptible. This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations to target for screening. We conduct a comprehensive analysis that considers the two most commonly adopted schemes: Individual testing and Dorfman group testing. For both schemes, we formulate an optimization model that aims to minimize the number of misclassifications under a testing capacity constraint. By analyzing the formulations, we establish key structural properties which we use to construct efficient and accurate solution techniques. We conduct a case study on COVID-19 in the United States using geographic-based data. Our results reveal that the considered proactive targeted schemes outperform commonly adopted practices by substantially reducing misclassifications. Our case study provides important managerial insights with regards to optimal allocation of tests, testing designs, and protocols that dictate the optimality of schemes. Such insights can inform policy-makers with tailored and implementable data-driven recommendations.

Pool testing with dilution effects and heterogeneous priors

The Dorfman pooled testing scheme is a process in which individual specimens (e.g., blood, urine, swabs, etc.) are pooled and tested together; if the merged sample tests positive for infection, then each specimen from the pool is tested individually. Through this procedure, laboratories can reduce the expected number of tests required to screen the population, as individual tests are only carried out when the pooled test detects an infection. Several different partitions of the population can be used to form the pools. In this study, we analyze the performance of ordered partitions, those in which subjects with similar probability of infection are pooled together. We derive sufficient conditions under which ordered partitions outperform other types of partitions in terms of minimizing the expected number of tests, the expected number of false negatives, and the expected number of false positive classifications. These sufficient conditions can be easily verified in practical applications once the dilution effect has been estimated. We also propose a measure of equity and present conditions under which this measure is maximized by ordered partitions.

Viral RNA in Mosquitoes (Diptera: Culicidae) Collected between 2019 and 2021 in Germany

Due to globalisation and climate change, mosquito-borne pathogens are emerging in new areas on all continents, including Europe, which has recently faced outbreaks of dengue, chikungunya and West Nile fever. The present study complements previous investigations to evaluate the circulation of mosquito-borne viruses in Germany, with the aim of identifying potential vector species and risk areas. Mosquitoes collected from 2019 to 2021 and identified to species or species group level were screened for viruses of the families Flaviviridae, Peribunyaviridae and the genus Alphavirus of the family Togaviridae. In total, 22,528 mosquitoes were examined, thus providing the most comprehensive study on West Nile virus (WNV) circulation so far in the German mosquito population. Usutu virus (USUV) RNA was detected in six samples, Sindbis virus (SINV) RNA in 21 samples and WNV RNA in 11 samples. Samples containing RNA of USUV and WNV consisted of mosquitoes collected in the East German federal states of Brandenburg, Saxony and Saxony-Anhalt, while samples with RNA of SINV originated from more widespread locations. Although minimum infection rates have remained relatively low, the intensity of virus circulation appears to be increasing compared to previous studies. Continuous mosquito screening contributes to the early detection of the introduction and spread of mosquito-borne pathogens.

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.

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.

Statistical modeling and evaluation of the impact of multiplicity classification thresholds on the COVID-19 pool testing accuracy

Prior research on pool testing focus on developing testing methods with the main objective of reducing the total number of tests. However, pool testing can also be used to improve the accuracy of the testing process. The objective of this paper is to improve the accuracy of pool testing using the same number of tests as that of individual testing taking into consideration the probability of testing errors and pool multiplicity classification thresholds. Statistical models are developed to evaluate the impact of pool multiplicity classiffcation thresholds on pool testing accuracy using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The findings indicate that under certain conditions, pool testing multiplicity yields superior testing accuracy compared to individual testing without additional cost. The results reveal that selecting the multiplicity classification threshold is a critical factor in improving the pool testing accuracy and show that the lower the prevalence level the higher the gains in accuracy using multiplicity pool testing. The findings also indicate that performance can be improved using a batch size that is inversely proportional to the prevalence level. Furthermore, the results indicate that multiplicity pool testing not only improves the testing accuracy but also reduces the total cost of the testing process. Based on the findings, the manufacturer's test sensitivity has more significant impact on the accuracy of multiplicity pool testing compared to that of manufacturer's test specificity.

Evaluation of pooling strategy of SARS-CoV-2 RT-PCR in limited resources setting in Egypt at low prevalence

Sample pooling testing for SARS-COV-2 can be an effective tool in COVID-19 screening when resources are limited, yet it is important to assess the performance before implementation as pooling has its limitations. Our objective was to assess the efficacy of pooling samples for coronavirus 2019 (COVID-19) compared to an individual analysis by using commercial platforms for nucleic acid testing. A total of 2200 nasopharyngeal swabs for SARS-COV-2 were tested individually and in pools of 4, 8, and 10. The cycle threshold (Ct) values of the positive pooled samples were compared to their corresponding individual positive samples. In pool size 10 samples, an estimated increase of 3-Ct was obtained, which led to false negative results in low viral load positive samples. Pooling SARS COV-2 samples is an effective strategy of screening to increase laboratories' capacity and reduce costs without affecting diagnostic performance. A pool size of 8 is recommended.

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.

Evaluation of sample pooling for the detection of SARS-CoV-2 in a resource-limited setting, Dominican Republic

INTRODUCTION

COVID-19 is a worldwide public health threat. Diagnosis by RT-PCR has been employed as the standard method to confirm viral infection. Sample pooling testing can optimize the resources by reducing the workload and reagents shortage, and be useful in laboratories and countries with limited resources. This study aims to evaluate SARS-CoV-2 detection by sample pooling testing in comparison with individual sample testing.

MATERIALS AND METHODS

We created 210 pools out of 245 samples, varying from 4 to 10 samples per pool, each containing a positive sample. We conducted detection of SARS-CoV-2-specific RdRp/E target sites.

RESULTS

Pooling of three samples for SARS-CoV-2 detection might be an efficient strategy to perform without losing RT-PCR sensitivity.

CONCLUSIONS

Considering the positivity rate in Dominican Republic and that larger sample pools have higher probabilities of obtaining false negative results, the optimal sample size to perform a pooling strategy shall be three samples.

Usefulness of Combining Sputum and Nasopharyngeal Samples for Viral Detection by Reverse Transcriptase PCR in Adults Hospitalized with Acute Respiratory Illness

Nasopharyngeal swabs (NPS) or washings have traditionally been used to diagnose respiratory tract infections. Reverse transcriptase PCR (RT-PCR) is widely used for rapid viral detection using samples from the upper respiratory tract. However, RT-PCR is rarely applied to sputum samples, mainly due to the viscosity of sputum. Thus, we assessed the detection rates of respiratory viruses from NPS, sputum samples, and combined NPS and sputum samples using multiplex RT-PCR (Allplex respiratory panels I, II, and III; Seegene, Seoul, South Korea). Paired NPS and sputum samples were collected from 219 patients admitted to the hospital with acute respiratory illnesses from October to December 2019. RT-PCR was performed on each sample for virus detection. Combined samples for virus detection were produced using remnant NPS and sputum samples with a positive virus signal. Respiratory viral nucleic acid was identified in 92 (42%) of 219 patients. Among the 92 viral detections, 61 (28%) were detected by both NPS and sputum samples. Twenty-four (11%) were sputum positive/NPS negative, and seven (3%) were sputum negative/NPS positive. For the combined NPS-sputum samples (n = 92), all paired samples positive in both specimens (n = 61) were also positive in the combined NPS-sputum sample. Twenty-seven (87%) of the 31 discordant paired samples were positive in the combined samples. Out of the total of 103 viruses identified before combining the samples, the detection rate of the combined samples was 94% (97/103), which was higher than the detection rates of sputum (88%; 91/103) and NPS (71%; 73/103). Because additional tests incur additional costs, our findings suggest that combining samples instead of testing separate samples using RT-PCR is likely the most cost-effective method of viral testing for patients with acute respiratory illnesses. IMPORTANCE This study reveals that RT-PCR utilizing sputum significantly increased the detection rate for respiratory viral nucleic acids among adult patients admitted to the hospital, compared to nasopharyngeal swabs (NPS). Notably, combined samples of sputum and NPS maintained the majority of the improved sputum detection rate with only a few positive signal losses from NPS samples. In order to detect respiratory viruses in adult patients with acute respiratory illness, it is important to choose the optimal respiratory samples. This study helped to improve our understanding of this process.

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.

SARS-CoV-2 surveillance by RT-qPCR-based pool testing of saliva swabs (lollipop method) at primary and special schools—A pilot study on feasibility and acceptability

Background

Since the onset of the COVID-19 pandemic, children have been mentally and physically burdened, particularly due to school closures, with an associated loss of learning. Therefore, efficient testing strategies with high sensitivity are necessary to keep schools open. Apart from individual rapid antigen testing, various methods have been investigated, such as PCR-based pool-testing of nasopharyngeal swabs, gargle, or saliva samples. To date, previous validation studies have found the PCR-based saliva swab pool testing method to be an effective screening method, however, the acceptability and feasibility of a widespread implementation in the school-setting among stakeholders has not been comprehensively evaluated.

Methods

In this pilot study, SARS-CoV-2 saliva swab pool testing of up to 15 swabs per pool was conducted in ten primary and special schools in Mecklenburg-Western Pomerania, Germany, over a period of one month. Thereafter, parents, teachers and school principals of the participating schools as well as the participating laboratories were surveyed about the feasibility and acceptability of this method, its large-scale implementation and challenges. Data were analyzed quantitatively and qualitatively.

Results

During the study period, 1,630 saliva swab pools were analyzed, of which 22 tested SARS-CoV-2 positive (1.3%). A total of N = 315 participants took part in the survey. Across all groups, the saliva swab pool testing method was perceived as more child-friendly (>87%), convenient (>82%), and easier (>81%) compared to rapid antigen testing by an anterior nasal swab. Over 80% of all participants favored widespread, regular use of the saliva swab method.

Conclusion

In school settings in particular, a high acceptability of the test method is crucial for a successful SARS-CoV-2 surveillance strategy. All respondents clearly preferred the saliva swab method, which can be used safely without complications in children six years of age and older. Hurdles and suggestions for improvement of an area-wide implementation were outlined.

Optimizing Pooled Testing for Estimating the Prevalence of Multiple Diseases

Pooled testing can enhance the efficiency of diagnosing individuals with diseases of low prevalence. Often, pooling is implemented using standard groupings (2, 5, 10, etc.). On the other hand, optimization theory can provide specific guidelines in finding the ideal pool size and pooling strategy. This article focuses on optimizing the precision of disease prevalence estimators calculated from multiplex pooled testing data. In the context of a surveillance application of animal diseases, we study the estimation efficiency (i.e., precision) and cost efficiency of the estimators with adjustments for the number of expended tests. This enables us to determine the pooling strategies that offer the highest benefits when jointly estimating the prevalence of multiple diseases, such as theileriosis and anaplasmosis. The outcomes of our work can be used in designing pooled testing protocols, not only in simple pooling scenarios but also in more complex scenarios where individual retesting is performed in order to identify positive cases. A software application using the shiny package in R is provided with this article to facilitate implementation of our methods. Supplementary materials accompanying this paper appear online.

Simulation of group testing scenarios can boost COVID-19 screening power

The COVID-19 has severely affected economies and health systems around the world. Mass testing could work as a powerful alternative to restrain disease dissemination, but the shortage of reagents is a limiting factor. A solution to optimize test usage relies on ‘grouping’ or ‘pooling’ strategies, which combine a set of individuals in a single reaction. To compare different group testing configurations, we developed the poolingr package, which performs an innovative hybrid in silico/in vitro approach to search for optimal testing configurations. We used 6759 viral load values, observed in 2389 positive individuals, to simulate a wide range of scenarios. We found that larger groups (>100) framed into multi-stage setups (up to six stages) could largely boost the power to detect spreaders. Although the boost was dependent on the disease prevalence, our method could point to cheaper grouping schemes to better mitigate COVID-19 dissemination through identification and quarantine recommendation for positive individuals.

A Regional Pooling Intervention in a High-Throughput COVID-19 Diagnostic Laboratory to Enhance Throughput, Save Resources and Time Over a Period of 6 Months

An effective and rapid diagnosis has great importance in tackling the ongoing COVID-19 pandemic through isolation of the infected individuals to curb the transmission and initiation of specialized treatment for the disease. It has been proven that enhanced testing capacities contribute to efficiently curbing SARS-CoV-2 transmission during the initial phases of the outbreaks. RT-qPCR is considered a gold standard for the diagnosis of COVID-19. However, in resource-limited countries expenses for molecular diagnosis limits the diagnostic capacities. Here, we present interventions of two pooling strategies as 5 sample pooling (P-5) and 10 sample pooling (P-10) in a high-throughput COVID-19 diagnostic laboratory to enhance throughput and save resources and time over a period of 6 months. The diagnostic capacity was scaled-up 2.15-folds in P-5 and 1.8-fold in P-10, reagents (toward RNA extraction and RT-qPCR) were preserved at 75.24% in P-5 and 86.21% in P-10, and time saved was 6,290.93 h in P-5 and 3147.3 h in P-10.

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.

Pooled testing of traced contacts under superspreading dynamics

Testing is recommended for all close contacts of confirmed COVID-19 patients. However, existing pooled testing methods are oblivious to the circumstances of contagion provided by contact tracing. Here, we build upon a well-known semi-adaptive pooled testing method, Dorfman's method with imperfect tests, and derive a simple pooled testing method based on dynamic programming that is specifically designed to use information provided by contact tracing. Experiments using a variety of reproduction numbers and dispersion levels, including those estimated in the context of the COVID-19 pandemic, show that the pools found using our method result in a significantly lower number of tests than those found using Dorfman's method. Our method provides the greatest competitive advantage when the number of contacts of an infected individual is small, or the distribution of secondary infections is highly overdispersed. Moreover, it maintains this competitive advantage under imperfect contact tracing and significant levels of dilution.

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.

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 Nasopharyngeal Swab Specimens to Improve Testing Capacity for SARS-CoV-2 by Real-Time RT-PCR

Background

The detection of SARS-CoV-2 using qRT-PCR with the pooling of samples can reduce workload and costs especially when the prevalence rate of COVID-19 in a population is low.

To analyse the effect of pooling samples on the sensitivity of RT-qPCR for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection, we compared the cycle threshold (Ct) values of pools of 5 and 10 that tested positive with Ct values of individual samples that tested positive in that pool. Twenty positive nasopharyngeal (NP) specimens with low to high viral load were selected and pooled individually with four and nine negative NP.

Results

In NP specimens, the sensitivity of pools of 5 and 10 were 90 and 85%, compared to individual sample testing, respectively. The RT-qPCR sensitivity of pools of 5 and 10 against individual testing were not significantly different (p > 0.05). Detection of positive samples with low Ct values (< 36) was consistently achieved in pools of 5 and 10. However, there were higher false negatives when samples with high ct values (> 36) were pooled and tested. The mean C_t values obtained with the 5-sample pooled testing exceeded individual sample testing by 1.85 ± 1.09 cycles, while C_t values obtained with the 10-sample pooling exceeded individual sample testing by 3.4 ± 1.65 cycles.

Conclusions

In a low prevalence setting, testing capacity can be increased by pooling 5 or 10 samples, but the risk of additional false negatives needs to be considered.

Evaluation of sample pooling for the detection of SARS-CoV-2 in a resource-limited setting, Dominican Republic

INTRODUCTION

COVID-19 is a worldwide public health threat. Diagnosis by RT-PCR has been employed as the standard method to confirm viral infection. Sample pooling testing can optimize the resources by reducing the workload and reagents shortage, and be useful in laboratories and countries with limited resources. This study aims to evaluate SARS-CoV-2 detection by sample pooling testing in comparison with individual sample testing.

MATERIALS AND METHODS

We created 210 pools out of 245 samples, varying from 4 to 10 samples per pool, each containing a positive sample. We conducted detection of SARS-CoV-2-specific RdRp/E target sites.

RESULTS

Pooling of three samples for SARS-CoV-2 detection might be an efficient strategy to perform without losing RT-PCR sensitivity.

CONCLUSIONS

Considering the positivity rate in Dominican Republic and that larger sample pools have higher probabilities of obtaining false negative results, the optimal sample size to perform a pooling strategy shall be three samples.

Testing at scale during the COVID-19 pandemic

Assembly and publication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome in January 2020 enabled the immediate development of tests to detect the new virus. This began the largest global testing programme in history, in which hundreds of millions of individuals have been tested to date. The unprecedented scale of testing has driven innovation in the strategies, technologies and concepts that govern testing in public health. This Review describes the changing role of testing during the COVID-19 pandemic, including the use of genomic surveillance to track SARS-CoV-2 transmission around the world, the use of contact tracing to contain disease outbreaks and testing for the presence of the virus circulating in the environment. Despite these efforts, widespread community transmission has become entrenched in many countries and has required the testing of populations to identify and isolate infected individuals, many of whom are asymptomatic. The diagnostic and epidemiological principles that underpin such population-scale testing are also considered, as are the high-throughput and point-of-care technologies that make testing feasible on a massive scale.

Pool testing on random and natural clusters of individuals: Optimisation of SARS-CoV-2 surveillance in the presence of low viral load samples

Facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. During the first emergency phase of the epidemic, RT-qPCR on nasopharyngeal (NP) swabs, which is the most reliable technique to detect ongoing infections, exhibited limitations due to availability of reagents and budget constraints. This stressed the need to develop screening procedures that require fewer resources and are suitable to be extended to larger portions of the population. RT-qPCR on pooled samples from individual NP swabs seems to be a promising technique to improve surveillance. We performed preliminary experimental analyses aimed to investigate the performance of pool testing on samples with low viral load and we evaluated through Monte Carlo (MC) simulations alternative screening protocols based on sample pooling, tailored to contexts characterized by different infection prevalence. We focused on the role of pool size and the opportunity to develop strategies that take advantage of natural clustering structures in the population, e.g. families, school classes, hospital rooms. Despite the use of a limited number of specimens, our results suggest that, while high viral load samples seem to be detectable even in a pool with 29 negative samples, positive specimens with low viral load may be masked by the negative samples, unless smaller pools are used. The results of MC simulations confirm that pool testing is useful in contexts where the infection prevalence is low. The gain of pool testing in saving resources can be very high, and can be optimized by selecting appropriate group sizes. Exploiting natural groups makes the definition of larger pools convenient and potentially overcomes the issue of low viral load samples by increasing the probability of identifying more than one positive in the same pool.

Sample pooling as a strategy for community monitoring for SARS-CoV-2

Sample pooling strategy was intended to determine the optimal parameters for group testing of pooled specimens for the detection of SARS-CoV-2 and process them without significant loss of test usability. Standard molecular diagnostic laboratory equipment, and commercially available centrifugal filters, RNA isolation kits and SARS Cov2 PCR tests were used. The basic idea was to combine and concentrate several samples to the maximal volume, which can be extracted with the single extraction column. Out of 16 tested pools, 12 were positive with cycle threshold (Ct) values within 0.5 and 3.01 Ct of the original individual specimens. The analysis of 112 specimens determined that 12 pools were positive, followed by identification of 6 positive individual specimens among the 112 tested. This testing was accomplished with the use of 16 extractions/PCR tests, resulting in saving of 96 reactions but adding the 40 centrifugal filters. The present study demonstrated that pool testing could detect even up to a single positive sample with Ct value as high as 34. According to the standard protocols, reagents and equipment, this pooling method can be applied easily in current clinical testing laboratories.