Pre-Vaccine Positivity of SARS-CoV-2 Antibodies in Alberta, Canada during the First Two Waves of the COVID-19 Pandemic
Bibliographic record
Abstract
Using SARS-CoV-2 serology testing, we assessed the proportion of people in Alberta, Canada (population 4.4 million) positive for COVID-19 antibodies, indicating previous infection, during the first two waves of the COVID-19 pandemic (prior to vaccination programs). Linking these results with sociodemographic population data provides valuable information as to which groups of the population are more likely to have been infected with the SARS-CoV-2 virus to help facilitate public health decision-making and interventions. We also compared seropositivity data with previous COVID-19 molecular testing results. Absence of antibody and molecular testing were highly correlated (95% negative concordance). Positive antibody correlation with a previous positive molecular test was low, suggesting the possibility of mild/asymptomatic infection or other reasons leading individuals from seeking medical attention. Our data highlight that the true estimate of population prevalence of COVID-19 is likely best informed by combining data from both serology and molecular testing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".