COVID Seroprevalence, Symptoms and Mortality During the First Wave of SARS-CoV-2 in Canada
Bibliographic record
Abstract
Abstract Background Efforts to stem Canada’s SARS-CoV-2 pandemic can benefit from direct understanding of the prevalence, infection fatality rates (IFRs), and information on asymptomatic infection. Methods We surveyed a representative sample of 19,994 adult Canadians about COVID symptoms and analyzed IgG antibodies against SARS-CoV-2 from self-collected dried blood spots (DBS) in 8,967 adults. A sensitive and specific chemiluminescence ELISA detected IgG to the spike trimer. We compared seroprevalence to deaths to establish IFRs and used mortality data to estimate infection levels in nursing home residents. Results The best estimate (high specificity) of adult seroprevalence nationally is 1.7%, but as high as 3.5% (high sensitivity) depending on assay cut-offs. The highest prevalence was in Ontario (2.4-3.9%) and in younger adults aged 18-39 years (2.5-4.4%). Based on mortality, we estimated 13-17% of nursing home residents became infected. The first viral wave infected 0.54-1.08 million adult Canadians, half of whom were <40 years old. The IFR outside nursing homes was 0.20-0.40%, but the COVID mortality rate in nursing home residents was >70 times higher than that in comparably-aged adults living in the community. Seropositivity correlated with COVID symptoms, particularly during March. Asymptomatic adults constituted about a quarter of definite seropositives, with a greater proportion in the elderly. Interpretation Canada had relatively low infection prevalence and low IFRs in the community, but not in nursing homes, during the first viral wave. Self-collected DBS for antibody testing is a practicable strategy to monitor the ongoing second viral wave and, eventually, vaccine-induced immunity among Canadian adults.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".