COVID-19 serological survey utilizing antenatal serum samples in British Columbia
Bibliographic record
Abstract
The COVID-19 pandemic, caused by SARS-CoV-2, highlighted the need for accurate and timely data on virus spread and immune responses at a population level. Serological surveys offer a comprehensive view of population-level immune response to SARS-CoV-2 post- infection and/or vaccination. Here, we performed a serial cross-sectional study from residual serum samples collected from pregnant individuals in British Columbia during their first trimester antenatal screening. A total of 28,050 samples were collected between November 2021 and March 2024. We tracked changes in antibody levels over time and examined differences in antibody responses based on age and vaccination status during different phases of the pandemic. Antenatal serum samples enabled tracking of SARS-CoV-2 serostatus within the population and waves of major SARS-CoV-2 infections, such as the Omicron surge in 2021-2022 and increases in infection during the 2023-2024 respiratory season. During the 2023-2024 season, we observed a significant rise in Nucleocapsid (N) seropositivity compared to the previous year, reaching 64.3 % in the vaccinated group and 67.05 % in the unvaccinated group. This suggests a high infection rate, likely driven by the latest Omicron variants. Additionally, we differentiated between infection-induced and vaccine-induced seroprevalence. By March 2024, Spike (S) seroprevalence was 94 % in the unvaccinated group and 100 % in the vaccinated group. We assessed the longevity of vaccine-induced antibody within the population. A significant negative correlation was observed between S seropositivity (indicative of vaccination without infection, S+/N-) and time since the last vaccine dose. In contrast, anti-N levels began to rise above the cut-off value of seropositivity 15 months post-vaccination, indicating increased infection rates and N seroprevalence as time post-vaccination increased. This serosurveillance approach provide critical insights for public health strategies for the future, emphasizing the importance of ongoing serosurveillance to help understand corelates of seroprotection at a population level and to support ongoing evidence-based vaccine policy.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".