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1. Protein-based lateral flow assays for COVID-19 detection

   https://doi.org/10.1093/protein/gzab010  

   Mahmoudinobar, Farbod; Britton, Dustin; Montclare, Jin Kim (January 2021, Protein Engineering, Design and Selection)

   null (Ed.)

   Abstract To combat the enduring and dangerous spread of COVID-19, many innovations to rapid diagnostics have been developed based on proteinprotein interactions of the SARS-CoV-2 spike and nucleocapsid proteins to increase testing accessibility. These antigen tests have most prominently been developed using the lateral flow assay (LFA) test platform which has the benefit of administration at point-of-care, delivering quick results, lower cost, and does not require skilled personnel. However, they have gained criticism for an inferior sensitivity. In the last year, much attention has been given to creating a rapid LFA test for detection of COVID-19 antigens that can address its high limit of detection while retaining the advantages of rapid antibodyantigen interaction. In this review, a summary of these proteinprotein interactions as well as the challenges, benefits, and recent improvements to protein based LFA for detection of COVID-19 are discussed. 

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2. A Machine Learning Study of COVID-19 Serology and Molecular Tests and Predictions

   Elkin, Magdalyn E.; Zhu, Xingquan (January 2022, Smart health)

   Serology and molecular tests are the two most commonly used methods for rapid COVID-19 infection testing. The two types of tests have different mechanisms to detect infection, by measuring the presence of viral SARS-CoV-2 RNA (molecular test) or detecting the presence of antibodies triggered by the SARS-CoV-2 virus (serology test). A handful of studies have shown that symptoms, combined with demographic and/or diagnosis features, can be helpful for the prediction of COVID-19 test outcomes. However, due to nature of the test, serology and molecular tests vary significantly. There is no existing study on the correlation between serology and molecular tests, and what type of symptoms are the key factors indicating the COVID-19 positive tests. In this study, we propose a machine learning based approach to study serology and molecular tests, and use features to predict test outcomes. A total of 2,467 donors, each tested using one or multiple types of COVID-19 tests, are collected as our testbed. By cross checking test types and results, we study correlation between serology and molecular tests. For test outcome prediction, we label 2,467 donors as positive or negative, by using their serology or molecular test results, and create symptom features to represent each donor for learning. Because COVID-19 produces a wide range of symptoms and the data collection process is essentially error prone, we group similar symptoms into bins. This decreases the feature space and sparsity. Using binned symptoms, combined with demographic features, we train five classification algorithms to predict COVID-19 test results. Experiments show that XGBoost achieves the best performance with 76.85% accuracy and 81.4% AUC scores, demonstrating that symptoms are indeed helpful for predicting COVID-19 test outcomes. Our study investigates the relationship between serology and molecular tests, identifies meaningful symptom features associated with COVID-19 infection, and also provides a way for rapid screening and cost effective detection of COVID-19 infection. 

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3. A robust pooled testing approach to expand COVID-19 screening capacity

   https://doi.org/10.1371/journal.pone.0246285  

   Bish, Douglas R.; Bish, Ebru K.; El-Hajj, Hussein; Aprahamian, Hrayer (February 2021, PLOS ONE)

   Pantea, Casian (Ed.)

   Limited testing capacity for COVID-19 has hampered the pandemic response. Pooling is a testing method wherein samples from specimens (e.g., swabs) from multiple subjects are combined into a pool and screened with a single test. If the pool tests positive, then new samples from the collected specimens are individually tested, while if the pool tests negative, the subjects are classified as negative for the disease. Pooling can substantially expand COVID-19 testing capacity and throughput, without requiring additional resources. We develop a mathematical model to determine the best pool size for different risk groups , based on each group’s estimated COVID-19 prevalence. Our approach takes into consideration the sensitivity and specificity of the test, and a dynamic and uncertain prevalence, and provides a robust pool size for each group. For practical relevance, we also develop a companion COVID-19 pooling design tool (through a spread sheet). To demonstrate the potential value of pooling, we study COVID-19 screening using testing data from Iceland for the period, February-28-2020 to June-14-2020, for subjects stratified into high- and low-risk groups. We implement the robust pooling strategy within a sequential framework, which updates pool sizes each week, for each risk group, based on prior week’s testing data. Robust pooling reduces the number of tests, over individual testing, by 88.5% to 90.2%, and 54.2% to 61.9%, respectively, for the low-risk and high-risk groups (based on test sensitivity values in the range [0.71, 0.98] as reported in the literature). This results in much shorter times, on average, to get the test results compared to individual testing (due to the higher testing throughput), and also allows for expanded screening to cover more individuals. Thus, robust pooling can potentially be a valuable strategy for COVID-19 screening. 

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4. Semi‐quantitative group testing for efficient and accurate qPCR screening of pathogens with a wide range of loads

   Nambiar, Ananthan; Pan, Chao; Rana, Vishal; Cheraghchi, Mahdi; Ribeiro, Joao; Maslov, Sergei; Milenkovic, Olgica (May 2024, BMC bioinformatics)

   Abstract Background: Results: To address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to e ciently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold (Ct) values into multiple bins, leveraging the information from the rst stage of screening to improve the detection sensitivity. Dynamic Ct threshold adjustments mitigate dilution e ects 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, o ers a promising solution for e cient 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. Keywords: Group testing, Pooled testing, Semiquantitative group testing, qPCR, Ct values, Viral load, COVID-19 

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5. Modeling COVID-19 spread in small colleges

   https://doi.org/10.1371/journal.pone.0255654  

   Bahl, Riti; Eikmeier, Nicole; Fraser, Alexandra; Junge, Matthew; Keesing, Felicia; Nakahata, Kukai; Reeves, Lily (August 2021, PLOS ONE)

   Gallos, Lazaros K. (Ed.)

   We develop an agent-based model on a network meant to capture features unique to COVID-19 spread through a small residential college. We find that a safe reopening requires strong policy from administrators combined with cautious behavior from students. Strong policy includes weekly screening tests with quick turnaround and halving the campus population. Cautious behavior from students means wearing facemasks, socializing less, and showing up for COVID-19 testing. We also find that comprehensive testing and facemasks are the most effective single interventions, building closures can lead to infection spikes in other areas depending on student behavior, and faster return of test results significantly reduces total infections. 

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