Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies

Implementation of sample pooling to strengthen the self-monitoring in the food industry: Case study of mycotoxins in cereals

Implementation of sample pooling strategy in the food chemical surveillance could lead to a strengthening of the food safety by increasing the number of analyzable samples. The analysis of a pool of samples and no longer individual samples was applied to the issue of self-monitoring mycotoxins in cereal-based foods, thanks to a data set provided by the surveillance based on ELISA-kits of two mycotoxins - zearalenone (1121 samples) and ochratoxin A (1601 samples) - in four different types of cereal products. After fitting the distribution of mycotoxin concentrations determined in this product category by a Pareto distribution and considering the measurement error in the decision threshold, numerical simulations of pooling were implemented using the Dorfman-2-step strategy. Simulations showed promising results for three out of the four case-studies of zearalenone and ochratoxin A. While being as sensitive and specific as the current one-by-one system, the pooling approach led to a reduction of the number of analyzes performed by 75 - 87 % in three out of the four case studies. Nevertheless, in unfavorable analytical conditions, the pooling approach can lead to an increase of the total number of analyzes.

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.

Development and validation of cost-effective SYBR Green-based RT-qPCR and its evaluation in a sample pooling strategy for detecting SARS-CoV-2 infection in the Indonesian setting

A low-cost SYBR Green-based RT-qPCR method to detect SARS-CoV-2 were developed and validated. Primers targeting a conserved and vital region of the N genes of SARS-CoV-2 were designed. In-silico study was performed to analyse the compatibility of the selected primer pair with Indonesian SARS-CoV-2 genome sequences available from the GISAID database. We determined the linearity of our new assay using serial dilution of SARS-CoV-2 RNA from clinical samples with known virus concentration. The assay was then evaluated using clinically relevant samples in comparison to a commercial TaqMan-based test kit. Finally, we applied the assay in sample pooling strategies for SARS-CoV-2 detection. The SYBR Green-based RT-qPCR method was successfully developed with sufficient sensitivity. There is a very low prevalence of genome variation in the selected N primer binding regions, indicating their high conservation. The validation of the assay using clinical samples demonstrated similar performance to the TaqMan method suggesting the SYBR methods is reliable. The pooling strategy by combining 5 RNA samples for SARS-CoV-2 detection using the SYBR RT-qPCR methods is feasible and provides a high diagnostic yield. However, when dealing with samples having a very low viral load, it may increase the risk of missing positive cases.

Implementation of large-scale pooled testing to increase rapid molecular diagnostic test coverage for tuberculosis: a retrospective evaluation

In 2021, only 6.4 million of the 10.6 million people with tuberculosis (TB) were diagnosed and treated for the disease. Although the World Health Organization recommends initial diagnostic testing using a rapid sensitive molecular assay, only 38% of people diagnosed with TB benefited from these, due to barriers including the high cost of available assays. Pooled testing has been used as an approach to increase testing efficiency in many resource-constrained situations, such as the COVID-19 pandemic, but it has not yet been widely adopted for TB diagnostic testing. Here we report a retrospective analysis of routine pooled testing of 10,117 sputum specimens using the Xpert MTB/RIF and Xpert MTB/RIF Ultra assays that was performed from July 2020 to February 2022. Pooled testing saved 48% of assays and enabled rapid molecular testing for 4156 additional people as compared to individual testing, with 6.6% of specimens positive for TB. From an in silico analysis, the positive percent agreement of pooled testing in pools of 3 as compared with individual testing for the Xpert MTB/RIF Ultra assay was estimated as 99.4% (95% CI, 96.6% to 100%). These results support the scale-up of pooled testing for efficient TB diagnosis.

Seasonal Patterns in the Frequency of Candidatus Liberibacter Asiaticus in Populations of Diaphorina citri (Hemiptera: Psyllidae) in Florida

Candidatus Liberibacter asiaticus (CLas) is one of the putative causal agents of huanglongbing, which is a serious disease in citrus production. The pathogen is transmitted by Diaphorina citri Kuwayama (Hemiptera: Psyllidae). As an observational study, six groves in central Florida and one grove at the southern tip of Florida were sampled monthly from January 2008 through February 2012 (50 months). The collected psyllids were sorted by sex and abdominal color. Disease prevalence in adults peaked in November, with a minor peak in February. Gray/brown females had the highest prevalence, and blue/green individuals of either sex had the lowest prevalence. CLas prevalence in blue/green females was highly correlated with the prevalence in other sexes and colors. Thus, the underlying causes for seasonal fluctuations in prevalence operated in a similar fashion for all psyllids. The pattern was caused by larger nymphs displacing smaller ones from the optimal feeding sites and immunological robustness in different sex-color morphotypes. Alternative hypotheses were also considered. Improving our understanding of biological interactions and how to sample them will improve management decisions. We agree with other authors that psyllid management is critical year-round.

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.

Adaptive group testing strategy for infectious diseases using social contact graph partitions

Mass testing is essential for identifying infected individuals during an epidemic and allowing healthy individuals to return to normal social activities. However, testing capacity is often insufficient to meet global health needs, especially during newly emerging epidemics. Dorfman's method, a classic group testing technique, helps reduce the number of tests required by pooling the samples of multiple individuals into a single sample for analysis. Dorfman's method does not consider the time dynamics or limits on testing capacity involved in infection detection, and it assumes that individuals are infected independently, ignoring community correlations. To address these limitations, we present an adaptive group testing (AGT) strategy based on graph partitioning, which divides a physical contact network into subgraphs (groups of individuals) and assigns testing priorities based on the social contact characteristics of each subgraph. Our AGT aims to maximize the number of infected individuals detected and minimize the number of tests required. After each testing round (perhaps on a daily basis), the testing priority is increased for each neighboring group of known infected individuals. We also present an enhanced infectious disease transmission model that simulates the dynamic spread of a pathogen and evaluate our AGT strategy using the simulation results. When applied to 13 social contact networks, AGT demonstrates significant performance improvements compared to Dorfman's method and its variations. Our AGT strategy requires fewer tests overall, reduces disease spread, and retains robustness under changes in group size, testing capacity, and other parameters. Testing plays a crucial role in containing and mitigating pandemics by identifying infected individuals and helping to prevent further transmission in families and communities. By identifying infected individuals and helping to prevent further transmission in families and communities, our AGT strategy can have significant implications for public health, providing guidance for policymakers trying to balance economic activity with the need to manage the spread of infection.

Theoretical Bounds on the Number of Tests in Noisy Threshold Group Testing Frameworks

We consider a variant of group testing (GT) models called noisy threshold group testing (NTGT), in which when there is more than one defective sample in a pool, its test result is positive. We deal with a variant model of GT where, as in the diagnosis of COVID-19 infection, if the virus concentration does not reach a threshold, not only do false positives and false negatives occur, but also unexpected measurement noise can reverse a correct result over the threshold to become incorrect. We aim to determine how many tests are needed to reconstruct a small set of defective samples in this kind of NTGT problem. To this end, we find the necessary and sufficient conditions for the number of tests required in order to reconstruct all defective samples. First, Fano’s inequality was used to derive a lower bound on the number of tests needed to meet the necessary condition. Second, an upper bound was found using a MAP decoding method that leads to giving the sufficient condition for reconstructing defective samples in the NTGT problem. As a result, we show that the necessary and sufficient conditions for the successful reconstruction of defective samples in NTGT coincide with each other. In addition, we show a trade-off between the defective rate of the samples and the density of the group matrix which is then used to construct an optimal NTGT framework.

Evaluation of conventional and point-of-care real-time RT-PCR tests for the detection of SARS-CoV-2 through a pooled testing strategy

Background

The rapid identification and isolation of individuals infected with SARS‐CoV‐2 are fundamental countermeasures for the efficient control of the COVID‐19 pandemic, which has affected millions of people around the world. Real‐time RT‐PCR is one of the most commonly applied reference methods for virus detection, and the use of pooled testing has been proposed as an effective way to increase the throughput of routine diagnostic tests. However, the clinical applicability of different types of real‐time RT‐PCR tests in a given group size remains inconclusive due to inconsistent regional disease prevalence and test demands.

Methods

In this study, the performance of one dual‐target conventional and two point‐of‐care real‐time RT‐PCR tests in a 5‐specimen pooled testing strategy for the detection of SARS‐COV‐2 was evaluated.

Results

We demonstrated the proof of concept that all of these real‐time RT‐PCR tests could feasibly detect SARS‐CoV‐2 from nasopharyngeal and oropharyngeal specimens that contain viral RNA loads in the range of 3.48 × 10⁵ to 3.42 × 10² copies/ml through pooled testing in a group size of 5 with overall positive percent agreement (pooling vs. individual testing) ranging from 100% to 93.75%. Furthermore, the two POC real‐time RT‐PCR tests exhibited comparable sensitivity to that of the dual‐target conventional one when clinical specimens were tested individually.

Conclusion

Our findings support the feasibility of using real‐time RT‐PCR tests developed as a variety of platforms in routine laboratory detection of suspected COVID‐19 cases through a pooled testing strategy that is beneficial to increasing the daily diagnostic capacity.

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 .

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.

Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies

Background: Due to the ongoing COVID-19 pandemic, demand for diagnostic testing has increased drastically, resulting in shortages of necessary materials to conduct the tests and overwhelming the capacity of testing laboratories. The supply scarcity and capacity limits affect test administration: priority must be given to hospitalized patients and symptomatic individuals, which can prevent the identification of asymptomatic and presymptomatic individuals and hence effective tracking and tracing policies. We describe optimized group testing strategies applicable to SARS-CoV-2 tests in scenarios tailored to the current COVID-19 pandemic and assess significant gains compared to individual testing. Methods: We account for biochemically realistic scenarios in the context of dilution effects on SARS-CoV-2 samples and consider evidence on specificity and sensitivity of PCR-based tests for the novel coronavirus. Because of the current uncertainty and the temporal and spatial changes in the prevalence regime, we provide analysis for several realistic scenarios and propose fast and reliable strategies for massive testing procedures. Key Findings: We find significant efficiency gaps between different group testing strategies in realistic scenarios for SARS-CoV-2 testing, highlighting the need for an informed decision of the pooling protocol depending on estimated prevalence, target specificity, and high- vs. low-risk population. For example, using one of the presented methods, all 1.47 million inhabitants of Munich, Germany, could be tested using only around 141 thousand tests if the infection rate is below 0.4% is assumed. Using 1 million tests, the 6.69 million inhabitants from the city of Rio de Janeiro, Brazil, could be tested as long as the infection rate does not exceed 1%. Moreover, we provide an interactive web application, available at www.grouptexting.com, for visualizing the different strategies and designing pooling schemes according to specific prevalence scenarios and test configurations. Interpretation: Altogether, this work may help provide a basis for an efficient upscaling of current testing procedures, which takes the population heterogeneity into account and is fine-grained towards the desired study populations, e.g., mild/asymptomatic individuals vs. symptomatic ones but also mixtures thereof. Funding: German Science Foundation (DFG), German Federal Ministry of Education and Research (BMBF), Chan Zuckerberg Initiative DAF, and Austrian Science Fund (FWF).