Canadian SARS-CoV-2 serological survey using antenatal serum samples: a retrospective seroprevalence study
Bibliographic record
Abstract
BACKGROUND: Insufficient data on the rate and distribution of SARS-CoV-2 infection in Canada has presented a substantial challenge to the public health response to the COVID-19 pandemic. Our objective was to assess SARS-CoV-2 seroprevalence in a representative sample of pregnant people throughout Canada, across multiple time points over 2 years of the pandemic, to describe the seroprevalence and show the ability of this process to provide prevalence estimates. METHODS: This Canadian retrospective serological surveillance study used existing serological prenatal samples across 10 provinces over multiple time periods: Feb. 3-21, 2020; Aug. 24-Sept. 11, 2020; Nov. 16-Dec. 4, 2020; Nov. 15-Dec. 3, 2021; and results from the province of British Columbia during a period in which the SARS-CoV-2 B.1.1.529 (Omicron) variant was predominant, from Nov. 15, 2021, to June 11, 2022. Age and postal code administrative data allowed for comparison with concurrent polymerase chain reactivity (PCR)-positive results collected by Statistics Canada and the Canadian Surveillance of COVID-19 in Pregnancy (CANCOVID-Preg) project. RESULTS: Seropositivity in antenatal serum as early as February 2020 indicates SARS-CoV-2 transmission before the World Health Organization's declaration of the pandemic. Seroprevalence in our sample of pregnant people was 1.84 to 8.90 times higher than the recorded concurrent PCR-positive prevalence recorded among females aged 20-49 years in November-December 2020. Overall seropositivity in our sample of pregnant people was low at the end of 2020, increasing to 15% in 1 province by the end of 2021. Seroprevalence among pregnant people in BC during the Omicron period increased from 5.8% to 43% from November 2021 to June 2022. INTERPRETATION: These results indicate widespread vulnerability to SARS-CoV-2 infection before vaccine availability in Canada. During the time periods sampled, public health tracking systems were under-reporting infections, and seroprevalence results during the Omicron period indicate extensive community spread of SARS-CoV-2 infection.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | medium |
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".