Array testing for multiplex assays

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.

binGroup2: Statistical Tools for Infection Identification via Group Testing

Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm's operating characteristics, such as the expected number of tests. Our paper presents the binGroup2 package that provides the statistical tools for this purpose. This R package is the first to address the identification aspect of group testing for a wide variety of algorithms. We illustrate its use through COVID-19 and chlamydia/gonorrhea applications of group testing.

Estimating the prevalence of two or more diseases using outcomes from multiplex group testing

When screening a population for infectious diseases, pooling individual specimens (e.g., blood, swabs, urine, etc.) can provide enormous cost savings when compared to testing specimens individually. In the biostatistics literature, testing pools of specimens is commonly known as group testing or pooled testing. Although estimating a population-level prevalence with group testing data has received a large amount of attention, most of this work has focused on applications involving a single disease, such as human immunodeficiency virus. Modern methods of screening now involve testing pools and individuals for multiple diseases simultaneously through the use of multiplex assays. Hou et al. (2017, Biometrics, 73, 656-665) and Hou et al. (2020, Biostatistics, 21, 417-431) recently proposed group testing protocols for multiplex assays and derived relevant case identification characteristics, including the expected number of tests and those which quantify classification accuracy. In this article, we describe Bayesian methods to estimate population-level disease probabilities from implementing these protocols or any other multiplex group testing protocol which might be carried out in practice. Our estimation methods can be used with multiplex assays for two or more diseases while incorporating the possibility of test misclassification for each disease. We use chlamydia and gonorrhea testing data collected at the State Hygienic Laboratory at the University of Iowa to illustrate our work. We also provide an online R resource practitioners can use to implement the methods in this article.

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.

Application of dried blood spot sample pooling strategies for Plasmodium 18S rRNA biomarker testing to facilitate identification of infected persons in large-scale epidemiological studies

Background

Plasmodium 18S rRNA is a sensitive biomarker for detecting Plasmodium infection in human blood. Dried blood spots (DBS) are a practical sample type for malaria field studies to collect, store, and transport large quantities of blood samples for diagnostic testing. Pooled testing is a common way to reduce reagent costs and labour. This study examined performance of the Plasmodium 18S rRNA biomarker assay for DBS, improved assay sensitivity for pooled samples, and created graphical user interface (GUI) programmes for facilitating optimal pooling.

Methods

DBS samples of varied parasite densities from clinical specimens, Plasmodium falciparum in vitro culture, and P. falciparum Armored RNA® were tested using the Plasmodium 18S rRNA quantitative triplex reverse transcription polymerase chain reaction (qRT-PCR) assay and a simplified duplex assay. DBS sample precision, linearity, limit of detection (LoD) and stability at varied storage temperatures were evaluated. Novel GUIs were created to model two-stage hierarchy, square matrix, and three-stage hierarchy pooling strategies with samples of varying positivity rates and estimated test counts. Seventy-eight DBS samples from persons residing in endemic regions with sub-patent infections were tested in pools and deconvoluted to identify positive cases.

Results

Assay performance showed linearity for DBS from 4 × 10⁷ to 5 × 10² parasites/mL with strong correlation to liquid blood samples (r² > 0.96). There was a minor quantitative reduction in DBS rRNA copies/mL compared to liquid blood samples. Analytical sensitivity for DBS was estimated 5.3 log copies 18S rRNA/mL blood (28 estimated parasites/mL). Properly preserved DBS demonstrated minimal degradation of 18S rRNA when stored at ambient temperatures for one month. A simplified duplex qRT-PCR assay omitting the human mRNA target showed improved analytical sensitivity, 1 parasite/mL blood, and was optimized for pooling. Optimal pooling sizes varied depending on prevalence. A pilot DBS study of the two-stage hierarchy pooling scheme corroborated results previously determined by testing individual DBS.

Conclusions

The Plasmodium 18S rRNA biomarker assay can be applied to DBS collected in field studies. The simplified Plasmodium qRT-PCR assay and GUIs have been established to provide efficient means to test large quantities of DBS samples.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03907-8.

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.

Informative array testing with multiplex assays

High-volume testing of clinical specimens for sexually transmitted diseases is performed frequently by a process known as group testing. This algorithmic process involves testing portions of specimens from separate individuals together as one unit (or "group") to detect diseases. Retesting is performed on groups that test positively in order to differentiate between positive and negative individual specimens. The overall goal is to use the least number of tests possible across all individuals without sacrificing diagnostic accuracy. One of the most efficient group testing algorithms is array testing. In its simplest form, specimens are arranged into a grid-like structure so that row and column groups can be formed. Positive-testing rows/columns indicate which specimens to retest. With the growing use of multiplex assays, the increasing number of diseases tested by these assays, and the availability of subject-specific risk information, opportunities exist to make this testing process even more efficient. We propose specific specimen arrangements within an array that can reduce the number of retests needed when compared with other array testing algorithms. We examine how to calculate operating characteristics, including the expected number of tests and the SD for the number of tests, and then subsequently find a best arrangement. Our methods are illustrated for chlamydia and gonorrhea detection with the Aptima Combo 2 Assay. We also provide R functions to make our research accessible to laboratories.