Serial cross-sectional estimation of vaccine-and infection-induced SARS-CoV-2 seroprevalence in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: The evolving proportion of the population considered immunologically naive versus primed for more efficient immune memory response to SARS-CoV-2 has implications for risk assessment. We sought to chronicle vaccine- and infection-induced seroprevalence across the first 7 waves of the COVID-19 pandemic in British Columbia, Canada. METHODS: During 8 cross-sectional serosurveys conducted between March 2020 and August 2022, we obtained anonymized residual sera from children and adults who attended an outpatient laboratory network in the Lower Mainland (Greater Vancouver and Fraser Valley). We used at least 3 immunoassays per serosurvey to detect SARS-CoV-2 spike and nucleocapsid antibodies. We assessed any seroprevalence (vaccineor infection-induced, or both), defined by positivity on any 2 assays, and infection-induced seroprevalence, also defined by dual-assay positivity but requiring both antinucleocapsid and antispike detection. We used estimates of infection-induced seroprevalence to explore underascertainment of infections by surveillance case reports. RESULTS: By January 2021, we estimated that any seroprevalence remained less than 5%, increasing with vaccine rollout to 56% by May-June 2021, 83% by September-October 2021 and 95% by March 2022. Infection-induced seroprevalence remained less than 15% through September-October 2021, increasing across Omicron waves to 42% by March 2022 and 61% by July-August 2022. By August 2022, 70%-80% of children younger than 20 years and 60%-70% of adults aged 20-59 years had been infected, but fewer than half of adults aged 60 years and older had been infected. Compared with estimates of infection-induced seroprevalence, surveillance case reports underestimated infections 12-fold between September 2021 and March 2022 and 92-fold between March 2022 and August 2022. INTERPRETATION: By August 2022, most children and adults younger than 60 years had evidence of both SARS-CoV-2 vaccination and infection. As previous evidence suggests that a history of both exposures may induce stronger, more durable hybrid immunity than either exposure alone, older adults - who have the lowest infection rates but highest risk of severe outcomes - continue to warrant prioritized vaccination.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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".