Can variants, reinfection, symptoms and test types affect COVID-19 diagnostic performance? A large-scale retrospective study of AG-RDTs during circulation of Delta and Omicron variants, Czechia, December 2021 to February 2022.

BackgroundThe sensitivity and specificity of selected antigen detection rapid diagnostic tests (AG-RDTs) for SARS-CoV-2 were determined in the unvaccinated population when the Delta variant was circulating. Viral loads, dynamics, symptoms and tissue tropism differ between Omicron and Delta.AimWe aimed to compare AG-RDT sensitivity and specificity in selected subgroups during Omicron vs Delta circulation.MethodsWe retrospectively paired AG-RDT results with PCRs registered in Czechia's Information System for Infectious Diseases from 1 to 25 December 2021 (Delta, n = 20,121) and 20 January to 24 February 2022 (Omicron, n = 47,104).ResultsWhen confirmatory PCR was conducted on the same day as AG-RDT as a proxy for antigen testing close to peak viral load, the average sensitivity for Delta was 80.4% and for Omicron 81.4% (p < 0.05). Sensitivity in vaccinated individuals was lower for Omicron (OR = 0.94; 95% confidence interval (CI): 0.87-1.03), particularly in reinfections (OR = 0.83; 95% CI: 0.75-0.92). Saliva AG-RDT sensitivity was below average for both Delta (74.4%) and Omicron (78.4%). Tests on the European Union Category A list had higher sensitivity than tests in Category B. The highest sensitivity for Omicron (88.5%) was recorded for patients with loss of smell or taste, however, these symptoms were almost 10-fold less common than for Delta. The sensitivity of AG-RDTs performed on initially asymptomatic individuals done 1, 2 or 3 days before a positive PCR test was consistently lower for Omicron compared with Delta.ConclusionSensitivity for Omicron was lower in subgroups that may become more common if SARS-CoV-2 becomes an endemic virus.

Role of population and test characteristics in antigen-based SARS-CoV-2 diagnosis, Czechia, August to November 2021.

BackgroundAnalyses of diagnostic performance of SARS-CoV-2 antigen rapid diagnostic tests (AG-RDTs) based on long-term data, population subgroups and many AG-RDT types are scarce.AimWe aimed to analyse sensitivity and specificity of AG-RDTs for subgroups based on age, incidence, sample type, reason for test, symptoms, vaccination status and the AG-RDT's presence on approved lists.MethodsWe included AG-RDT results registered in Czechia's Information System for Infectious Diseases between August and November 2021. Subpopulations were analysed based on 346,000 test results for which a confirmatory PCR test was recorded ≤ 3 days after the AG-RDT; 38 AG-RDTs with more than 100 PCR-positive and 300 PCR-negative samples were individually evaluated.ResultsAverage sensitivity and specificity were 72.4% and 96.7%, respectively. We recorded lower sensitivity for age groups 0-12 (65.5%) and 13-18 years (65.3%). The sensitivity level rose with increasing SARS-CoV-2 incidence from 66.0% to 76.7%. Nasopharyngeal samples had the highest sensitivity and saliva the lowest. Sensitivity for preventive reasons was 63.6% vs 86.1% when testing for suspected infection. Sensitivity was 84.8% when one or more symptoms were reported compared with 57.1% for no symptoms. Vaccination was associated with a 4.2% higher sensitivity. Significantly higher sensitivity levels pertained to AG-RDTs on the World Health Organization Emergency Use List (WHO EUL), European Union Common List and the list of the United Kingdom's Department of Health and Social Care.ConclusionAG-RDTs from approved lists should be considered, especially in situations associated with lower viral load. Results are limited to SARS-CoV-2 delta variant.

Explainability of Text Clustering Visualizations-Twitter Disinformation Case Study.

While text clustering methods have been available for decades, there is a paucity of material that would help practitioners with the choice and configuration of suitable algorithms and visualizations. In this article, we present a case study analyzing two disinformation datasets composed of tweets from the era of the 2016 United States Presidential Election. We use this to demonstrate steps for selecting the best configuration of the clustering algorithm and consequently conduct a user experiment for evaluating the comprehensibility of three alternate visualizations. A supplementary GitHub repository contains source code with examples.

Why was this cited? Explainable machine learning applied to COVID-19 research literature.

Multiple studies have investigated bibliometric factors predictive of the citation count a research article will receive. In this article, we go beyond bibliometric data by using a range of machine learning techniques to find patterns predictive of citation count using both article content and available metadata. As the input collection, we use the CORD-19 corpus containing research articles-mostly from biology and medicine-applicable to the COVID-19 crisis. Our study employs a combination of state-of-the-art machine learning techniques for text understanding, including embeddings-based language model BERT, several systems for detection and semantic expansion of entities: ConceptNet, Pubtator and ScispaCy. To interpret the resulting models, we use several explanation algorithms: random forest feature importance, LIME, and Shapley values. We compare the performance and comprehensibility of models obtained by "black-box" machine learning algorithms (neural networks and random forests) with models built with rule learning (CORELS, CBA), which are intrinsically explainable. Multiple rules were discovered, which referred to biomedical entities of potential interest. Of the rules with the highest lift measure, several rules pointed to dipeptidyl peptidase4 (DPP4), a known MERS-CoV receptor and a critical determinant of camel to human transmission of the camel coronavirus (MERS-CoV). Some other interesting patterns related to the type of animal investigated were found. Articles referring to bats and camels tend to draw citations, while articles referring to most other animal species related to coronavirus are lowly cited. Bat coronavirus is the only other virus from a non-human species in the betaB clade along with the SARS-CoV and SARS-CoV-2 viruses. MERS-CoV is in a sister betaC clade, also close to human SARS coronaviruses. Thus both species linked to high citation counts harbor coronaviruses which are more phylogenetically similar to human SARS viruses. On the other hand, feline (FIPV, FCOV) and canine coronaviruses (CCOV) are in the alpha coronavirus clade and more distant from the betaB clade with human SARS viruses. Other results include detection of apparent citation bias favouring authors with western sounding names. Equal performance of TF-IDF weights and binary word incidence matrix was observed, with the latter resulting in better interpretability. The best predictive performance was obtained with a "black-box" method-neural network. The rule-based models led to most insights, especially when coupled with text representation using semantic entity detection methods. Follow-up work should focus on the analysis of citation patterns in the context of phylogenetic trees, as well on patterns referring to DPP4, which is currently considered as a SARS-Cov-2 therapeutic target.