Comparison of the analytical and clinical sensitivities of 34 rapid antigen tests with prevalent SARS-CoV-2 variants of concern during the COVID-19 pandemic in the UK
Bibliographic record
Abstract
Antigen-detection rapid diagnostic tests (Ag-RDTs) have become a central pillar for the management of coronavirus disease worldwide due to their speed and ease of use and are now being developed for use in other emerging outbreaks. Like other viruses, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is subject to rapid mutation as it spreads, and new variants of concern (VOCs) emerge frequently, posing a significant challenge for the detection of newer, highly mutated variants. It is, therefore, important that the performance of Ag-RDTs is regularly evaluated, particularly in outbreak scenarios where rapid diagnostics are key to limiting disease spread. Here, we present a comprehensive evaluation of the analytical and clinical sensitivities of 34 commercially available Ag-RDTs with five SARS-CoV-2 VOCs, all of which were highly prevalent in the UK at various times between 2019 and 2023. This study highlights the importance of regular evaluation of the Ag-RDT performance, with several Ag-RDTs demonstrating a reduced performance with some VOCs. We conclude that a regular performance evaluation through our proposed pipeline, combined with a broad consensus approval threshold across global organizations, is essential to maintaining the effectiveness of Ag-RDTs as a disease management tool during outbreaks.IMPORTANCEAntigen-detection rapid diagnostic tests (Ag-RDTs) came to global prominence during the coronavirus disease pandemic, where they offered a quick and simple at-home diagnostic, which could be used to manage disease spread. A major ongoing challenge for the broad use of Ag-RDTs is the speed at which new SARS-CoV-2 variants emerge, each of which has the potential to reduce the performance of available Ag-RDTs. As Ag-RDTs are explored for use in other viral disease outbreaks, pipelines for the regular evaluation of test performance are essential for ensuring that Ag-RDTs can be employed effectively. Here, we have developed a robust pipeline for the large-scale evaluation of commercially available Ag-RDTs against several major SARS-CoV-2 variants, which can be adapted and applied to other emerging outbreaks to ensure that test performance is maintained as a virus evolves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".