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The group testing idea is an efficient infection identification approach based on pooling the test samples of a group of individuals, which results in identification with less number of tests than individually testing the population. In our work, we propose a novel infection spread model based on a random connection graph which represents connections between n individuals. Infection spreads via connections between individuals, and this results in a probabilistic cluster formation structure as well as non-i.i.d. (correlated) infection statuses for individuals. We propose a class of two-step sampled group testing algorithms where we exploit the known probabilistic infection spread model. We investigate the metrics associated with two-step sampled group testing algorithms. To demonstrate our results, for analytically tractable exponentially split cluster formation trees, we calculate the required number of tests and the expected number of false classifications in terms of the system parameters, and identify the trade-off between them. For such exponentially split cluster formation trees, for zero-error construction, we prove that the required number of tests is O(log2n). Thus, for such cluster formation trees, our algorithm outperforms any zero-error non-adaptive group test, binary splitting algorithm, and Hwang’s generalized binary splitting algorithm. Our results imply that, by exploiting probabilistic information on the connections of individuals, group testing can be used to reduce the number of required tests significantly even when the infection rate is high, contrasting the prevalent belief that group testing is useful only when the infection rate is low.

Group testing idea is an efficient approach to detect prevalence of an infection in the test samples taken from a group of individuals. It is based on the idea of pooling the test samples and performing tests to the mixed samples. This approach results in possible reduction in the required number of tests to identify infections. Classical group testing works consider static settings where the infection statuses of the individuals do not change throughout the testing process. In our paper, we study a dynamic infection spread model, inspired by the discrete time SIR model, where infections are spread via non-isolated infected individuals, while infection keeps spreading over time, a limited capacity testing is performed at each time instance as well. In contrast to the classical, static group testing problem, the objective in our setup is not to find the minimum number of required tests to identify the infection status of every individual in the population, but to control the infection spread by detecting and isolating the infections over time by using the given, limited number of tests. In order to analyze the performance of the proposed algorithms, we focus on the average-case analysis of the number of individuals that remain non-infected throughout the process of controlling the infection. We propose two dynamic algorithms that both use given limited number of tests to identify and isolate the infections over time, while the infection spreads, while the first algorithm is a dynamic randomized individual testing algorithm, in the second algorithm we employ the group testing approach similar to the original work of Dorfman. By considering weak versions of our algorithms, we obtain lower bounds for the performance of our algorithms. Finally, we implement our algorithms and run simulations to gather numerical results and compare our algorithms and theoretical approximation results under different sets of system parameters.

Background Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative information provided by qPCR methods, nor is it able to accommodate a wide range of pathogen loads. Results To address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold ( Ct ) values into multiple bins, leveraging the information from the first stage of screening to improve the detection sensitivity. Dynamic Ct threshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by 24% on the only complete real-world qPCR group testing dataset from Israel, while maintaining a negligible false negative rate. Conclusion In conclusion, our adaptive SQGT approach, utilizing qPCR data and dynamic threshold adjustments, offers a promising solution for efficient population screening. With a reduction in the number of tests and minimal false negatives, SQGT holds potential to enhance disease control and testing strategies on a global scale.

Group testing is an efficient algorithmic approach to the infection identification problem, based on mixing the test samples and testing the mixed samples instead of individually testing each sample. In this paper, we consider the dynamic infection spread model that is based on the discrete SIR model, which assumes the disease to be spread over time via infected and non-isolated individuals. In our system, the main objective is not to minimize the number of required tests to identify every infection, but instead, to utilize the available, given testing capacity T at each time instance to efficiently control the infection spread. We introduce and study a novel performance metric, which we coin as ϵ-disease control time. This metric can be used to measure how fast a given algorithm can control the spread of a disease. We characterize the performance of the dynamic individual testing algorithm and introduce a novel dynamic SAFFRON-based group testing algorithm. We present theoretical results and implement the proposed algorithms to compare their performances.

Abstract Objectives To establish the optimal parameters for group testing of pooled specimens for the detection of SARS-CoV-2. Methods The most efficient pool size was determined to be five specimens using a web-based application. From this analysis, 25 experimental pools were created using 50 µL from one SARS-CoV-2 positive nasopharyngeal specimen mixed with 4 negative patient specimens (50 µL each) for a total volume of 250 µL. Viral RNA was subsequently extracted from each pool and tested using the CDC SARS-CoV-2 RT-PCR assay. Positive pools were consequently split into individual specimens and tested by extraction and PCR. This method was also tested on an unselected group of 60 nasopharyngeal specimens grouped into 12 pools. Results All 25 pools were positive with cycle threshold (Ct) values within 0 and 5.03 Ct of the original individual specimens. The analysis of 60 specimens determined that 2 pools were positive followed by identification of 2 individual specimens among the 60 tested. This testing was accomplished while using 22 extractions/PCR tests, a savings of 38 reactions. Conclusions When the incidence rate of SARS-CoV-2 infection is 10% or less, group testing will result in the saving of reagents and personnel time with an overall increase in testing capability of at least 69%.

In this study, the authors propose a new group testing based (GTB) error control codes (ECCs) approach for improving the reliability of memory structures in computing systems. Compared with conventional single- and double-bit error correcting codes, the GTB codes provide higher reliability at the multi-byte error correction granularity. The proposed codes are cost-efficient in their encoding and decoding procedures. Instead of requiring multiplication or inversion over Galois finite field like most multi-byte ECC schemes, the proposed technique only involves bitwise XOR operations, therefore, significantly reducing the computation complexity and latency. For instance, to correct m errors in a Q-ary codeword of length N, where $Q \\ge 2$Q≥2, the compute complexity is mere $O\\lpar mN\\log Q\\rpar $O(mNlog⁡Q). The GTB codes trade redundancy for encoding and decoding simplicity, and are able to achieve better code rate than other ECCs of the same trade-off. The proposed GTB codes lend themselves well to designs with high reliability and low computation complexity requirements, such as storage systems with strong fault tolerance, or compute systems with straggler tolerance, and so on.

We propose a family of tests to assess the goodness of fit of a high dimensional generalized linear model. Our framework is flexible and may be used to construct an omnibus test or directed against testing specific non-linearities and interaction effects, or for testing the significance of groups of variables. The methodology is based on extracting left-over signal in the residuals from an initial fit of a generalized linear model. This can be achieved by predicting this signal from the residuals by using modern powerful regression or machine learning methods such as random forests or boosted trees. Under the null hypothesis that the generalized linear model is correct, no signal is left in the residuals and our test statistic has a Gaussian limiting distribution, translating to asymptotic control of type I error. Under a local alternative, we establish a guarantee on the power of the test.We illustrate the effectiveness of the methodology on simulated and real data examples by testing goodness of fit in logistic regression models. Software implementing the methodology is available in the R package GRPtests.

Group testing (i.e., testing multiple subjects simultaneously with a single test) is essential for classifying a large population of subjects as positive or negative for a binary characteristic (e.g., presence of a disease). We study optimal group testing designs under subject-specific risk characteristics and imperfect tests, considering classification accuracy-, efficiency- and equity-based objectives, and characterize important structural properties of optimal testing designs. These properties allow us to model the testing design problems as partitioning problems, develop efficient algorithms, and derive insights on equity versus accuracy trade-off. One of our models reduces to a constrained shortest path problem, for a special case of which we develop a polynomial-time algorithm. We also show that determining an optimal risk-based Dorfman testing scheme that minimizes the expected number of tests is tractable, resolving an open conjecture. We demonstrate the value of optimal risk-based testing schemes with a case study of public health screening. This paper was accepted by Yinyu Ye, optimization.

Background Estimating an individual’s ethnicity from genetic data is crucial for analyzing disease association studies, making informed medical decisions, conducting forensic investigations, and tracing genealogical ancestry. Results This work combines non-adaptive group testing using the mathematical field of compressed sensing and standard short-read sequencing to allow an up to 4-fold increase in the number of samples in large-scale ethnicity estimates. The method requires no prior knowledge regarding the tested individuals and provides almost identical results compared to testing each individual independently. Our results are based on simulated data, and on simulations based on experimental data from the 1000 Genomes Project and the Human Genome Diversity Project. Conclusions Our computational approach aims to reduce the costs of large-scale ancestry testing by up to 4-fold in many real-life scenarios while not compromising accuracy. We hope this method will allow more efficient large-scale ethnicity screenings.

Background Clinical reasoning skills are essential competencies for medical students; therefore, effective, evidence-informed teaching methodologies are needed worldwide. This study investigated the benefits of team-based learning (TBL) for developing the skills in medical students. Method A mixed-methods sequential explanatory design was used to investigate the effectiveness of TBL for medical students acquiring clinical reasoning skills. The study participants comprised 92 fourth-year medical students at Yokohama City University School of Medicine, participating in TBL sessions that covered 10 major clinical symptoms identified in the core curriculum. Each session lasted 240 min. Before and after the educational intervention, student performance was measured using the script concordance test (SCT) on a 30-point scale, and self-assessed clinical reasoning competency was measured on a 7-point Likert scale. The SCT included pre-tests and post-tests of 30 questions each, with students randomly assigned to one of two test sets. Following the quantitative evaluation, a qualitative content analysis was conducted to explore the advantages of TBL for learning clinical reasoning skills. The analytic categories were set according to the six levels of Fink’s taxonomy of significant learning. Result Student performance improved significantly after the educational intervention (A test: 16.5 ± 4.4 to 18.7 ± 4.5, p  = 0.019; B test: 18.1 ± 3.7 to 19.8 ± 4.4, p  = 0.028). After the educational intervention, self-assessed clinical reasoning competency was significantly higher in “recalling appropriate physical examination and tests on clinical hypothesis generation,” “recalling appropriate differential diagnosis from patient’s chief complaint,” “verbalizing points that fit/don’t fit the recalled differential diagnosis appropriately,” “verbalizing and reflecting appropriately on own mistakes,” “selecting keywords from the whole aspect of the patient,” and “practicing the appropriate clinical reasoning process” (all p  < 0.001). The content analysis extracted 23 subcategories and 233 codes of the advantages of TBL for learning clinical reasoning skills, covering all six levels of Fink’s taxonomy of significant learning: Foundational knowledge (7 codes); Application (40 codes); Integration (69 codes); Human dimension (89 codes); Caring (8 codes); and Learning how to learn (20 codes). Conclusion This study demonstrates that TBL supports the acquisition of critical clinical reasoning skills among medical students.