Development and use of a method based on the anti-N reactivity of longitudinal samples to better estimate SARS-CoV-2 seroprevalence in a vaccinated population
Bibliographic record
Abstract
ABSTRACT Background Emerging evidence suggests that COVID-19 vaccination decreases the sensitivity of anti-nucleocapsid (N) serologies, making them less reliable to assess recently-acquired infections. We therefore developed and tested a new approach based on the ratio of the anti-N absorbance of longitudinal samples to overcome this limitation. Methods Previously vaccinated repeat plasma donors provided at least one pre-infection (reference) and one post-infection (test) sample. All samples were tested using an in-house anti-N ELISA. Seropositivity was determined based on the ratio between the anti-N absorbance of the test and reference samples. The ratio approach was tested in a real-world setting during three cross-sectional serosurveys carried out among plasma donors in Québec, Canada. Results Using a cut-off ratio of 1.5, the approach had a sensitivity of 95.2% among the 248 previously vaccinated and infected donors compared with 63.3% for the conventional approach. When tested in a real-world setting, the ratio-based approach yielded an adjusted seroprevalence of 27.4% (95% confidence interval [CI]=23.8%-30.9%) at the latest time point considered, compared to 15.1% (95% CI=12.2%-18.0%) for the conventional approach. Conclusions This article describes a new and highly-sensitive approach that captures a significantly greater proportion of vaccinated individuals with a recent history of SARS-CoV-2 infection.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".