Serial cross-sectional estimation of vaccine and infection-induced SARS-CoV-2 sero-prevalence in children and adults, British Columbia, Canada: March 2020 to August 2022
Bibliographic record
Abstract
ABSTRACT Background We chronicle SARS-CoV-2 sero-prevalence through eight cross-sectional sero-surveys (snapshots) in the Lower Mainland (Greater Vancouver and Fraser Valley), British Columbia, Canada from March 2020 to August 2022. Methods Anonymized-residual sera were obtained from children and adults attending an outpatient laboratory network. Sera were tested with at least three immuno-assays per snapshot to detect spike (S1) and/or nucleocapsid protein (NP) antibodies. Sero-prevalence was defined by dual-assay positivity, including any or infection-induced, the latter requiring S1+NP antibody detection from January 2021 owing to vaccine availability. Infection-induced estimates were used to assess the extent to which surveillance case reports under-estimated infections. Results Sero-prevalence was ≤1% by the 3 rd snapshot in September 2020 and <5% by January 2021 (4 th ). Following vaccine roll-out, sero-prevalence increased to >55% by May/June 2021 (5 th ), ∼80% by September/October 2021 (6 th ), and >95% by March 2022 (7 th ). In all age groups, infection-induced sero-prevalence remained <15% through September/October 2021, increasing through subsequent Omicron waves to ∼40% by March 2022 (7 th ) and ∼60% by July/August 2022 (8 th ). By August 2022, at least 70-80% of children ≤19 years, 60-70% of adults 20-59 years, but ∼40% of adults ≥60 years had been infected. Surveillance case reports under-estimated infections by 12-fold between the 6 th -7 th and 92-fold between the 7 th -8 th snapshots. Interpretation By August 2022, most children and adults had acquired SARS-CoV-2 vaccine and infection exposures, resulting in more robust hybrid immunity. Conversely the elderly, still at greatest risk of severe outcomes, remain largely-dependent on vaccine-induced protection alone, and should be prioritized for additional doses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".