A Population-Based Study of SARS-CoV-2 IgG Antibody Responses to Vaccination in Manitoba
Bibliographic record
Abstract
Understanding variables that influence antibody responses to COVID-19 vaccination within a population can provide valuable information on future vaccination strategies. In this population-based study, we examined the antibody responses to COVID-19 vaccination in Manitoba using residual serum specimens collected between January 2021 and March 2022 (n = 20,365). Samples were tested for spike and nucleocapsid IgG against SARS-CoV-2 using clinically validated assays. We assessed the impacts of multiple factors on post-vaccination antibody titres including type of vaccine, age, sex, geographic location, number of doses received, and timing of vaccination. Our investigation demonstrated that vaccination with one dose of Moderna mRNA-1273 elicited higher anti-spike IgG titres overall compared to Pfizer BNT162b2 vaccination, while one dose of Pfizer BNT162b2 followed by a second dose of Moderna mRNA-1273 exhibited higher titres than two doses of Pfizer BNT162b2 or Moderna mRNA-1273, irrespective of age. Age and time post-vaccination had considerable effects on antibody responses, with older age groups exhibiting lower anti-spike IgG titres than younger ages, and titres of those vaccinated with Pfizer BNT162b2 waning faster than those vaccinated with Moderna mRNA-1273 or a combination of Pfizer BNT162b2 and Moderna mRNA-1273. Antibody titres did not appear to be affected by sex or geographic location. Our results identify how factors such as age and type of vaccine can influence antibody responses to vaccination, and how antibody titres wane over time. This information highlights the importance of tailoring vaccine regimens to specific populations, especially those at increased risk of severe COVID-19 and can be used to inform future vaccination strategies, scheduling of booster doses, and public health measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".