Performance of rapid antigen tests in identifying Omicron BA.4 and BA.5 infections in South Africa
Bibliographic record
Abstract
BACKGROUND: Concerns around accuracy and performance of rapid antigen tests continue to be raised with the emergence of new SARS-CoV-2 variants. OBJECTIVE: To evaluate the performance of two widely used SARS-CoV-2 rapid antigen tests during BA.4/BA.5 SARS-CoV-2 wave in South Africa (May - June 2022). STUDY DESIGN: A prospective field evaluation compared the SARS-CoV-2 Antigen Rapid test from Hangzhou AllTest Biotech (nasal swab) and the Standard Q COVID-19 Rapid Antigen test from SD Biosensor (nasopharyngeal swab) to the Abbott RealTime SARS-CoV-2 assay (nasopharyngeal swab) on samples collected from 540 study participants. RESULTS: Overall 28.52% (154/540) were SARS-CoV-2 RT-PCR positive with median cycle number value of 12.30 (IQR 9.30-19.40). Out of the 99 successfully sequenced SARS-CoV-2 positive samples, 18 were classified as BA.4 and 56 were classified as BA.5. The overall sensitivities of the AllTest SARS-CoV-2 Ag test and Standard Q COVID-19 Ag test were 73.38% (95% CI 65.89-79.73) and 74.03% (95% CI 66.58-80.31) and their specificities were 97.41% (95% CI 95.30-98.59) and 99.22% (95% CI 97.74-99.74) respectively. Sensitivity was >90% when the cycle number value was <20. The sensitivity of both rapid tests was >90% in samples infected with Omicron sub-lineage BA.4 and BA.5. CONCLUSION: Accuracy of tested rapid antigen tests that target the nucleocapsid SARS-CoV-2 protein, were not adversely affected by BA.4 and BA.5 Omicron sub-variants.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".