Assessment of population infection with SARS-CoV-2 in Ontario, Canada, March to June 2020
Bibliographic record
Abstract
BackgroundSerosurveys for SARS-CoV-2 aim to estimate the proportion of the population that has been infected.AimThis observational study assesses the seroprevalence of SARS-CoV-2 antibodies in Ontario, Canada during the first pandemic wave.MethodsUsing an orthogonal approach, we tested 8,902 residual specimens from the Public Health Ontario laboratory over three time periods during March-June 2020 and stratified results by age group, sex and region. We adjusted for antibody test sensitivity/specificity and compared with reported PCR-confirmed COVID-19 cases.ResultsAdjusted seroprevalence was 0.5% (95% confidence interval (CI): 0.1-1.5) from 27 March-30 April, 1.5% (95% CI: 0.7-2.2) from 26-31 May, and 1.1% (95% CI: 0.8-1.3) from 5-30 June 2020. Adjusted estimates were highest in individuals aged ≥ 60 years in March-April (1.3%; 95% CI: 0.2-4.6), in those aged 20-59 years in May (2.1%; 95% CI: 0.8-3.4) and in those aged ≥ 60 years in June (1.6%; 95% CI: 1.1-2.1). Regional seroprevalence varied, and was highest for Toronto in March-April (0.9%; 95% CI: 0.1-3.1), for Toronto in May (3.2%; 95% CI: 1.0-5.3) and for Toronto (1.5%; 95% CI: 0.9-2.1) and Central East in June (1.5%; 95% CI: 1.0-2.0). We estimate that COVID-19 cases detected by PCR in Ontario underestimated SARS-CoV-2 infections by a factor of 4.9.ConclusionsOur results indicate low population seroprevalence in Ontario, suggesting that public health measures were effective at limiting the spread of SARS-CoV-2 during the first pandemic wave.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".