Omicron incidence and seroprevalence among children in Montreal, Canada, in early 2023: final results from the longitudinal EnCORE serology study
Bibliographic record
Abstract
Since early 2022, routine testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on symptoms and exposure history has largely ceased in Canada. Consequently, seroprevalence studies, particularly longitudinal studies, have become critical for monitoring the rate of incident SARS-CoV-2 infections and the proportion of the population with evidence of immunity. EnCORE is a longitudinal SARS-CoV-2 seroprevalence study comprising five rounds of serology testing from October 2020 to June 2023, in a sample of 2- to 17-year-olds (at baseline), recruited from daycares and schools in four neighbourhoods of Montreal, Canada. We report on SARS-CoV-2 incidence and seroprevalence among the 509 participants in the fifth and final round of the study. Seroprevalence of antibodies from either infection or vaccination was 98% (95 per cent confidence interval [CI]: 97, 99). The infection-acquired seroprevalence was 78% (95% CI: 73-82), and the incidence rate was 113 per 100 person-years (95% CI: 94-132), compared to the seroprevalence of 58% and the incidence rate of 133 per 100 person-years, respectively, in the fourth round of testing (mid-late 2022). Of the 131 participants newly seropositive for infection in Round 4, only 18 were seronegative for infection in Round 5 (median follow-up: 326 days).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".