A national study of self-reported COVID symptoms during the first viral wave in Canada
Bibliographic record
Abstract
Abstract Importance Accurate understanding of COVID pandemic during the first viral wave in Canada could help prepare for future epidemic waves. Objective To track the early course of the pandemic by examining self-reported COVID symptoms over time before testing became widely available. Design Adults from the nationally representative Angus Reid Forum were randomly invited to complete an online survey in May/June 2020. The study is a part of the Action to Beat Coronavirus antibody testing study. Setting A 20-item internet survey. Participants 14,408 adults age 18 years of age. Exposures The months that respondents and any household members first experienced various respiratory, neurological, sleep, skin or gastric symptoms. Main Outcomes and Measure “COVID symptom-positive,” defined as fever (or fever with hallucinations) plus at least one of difficulty breathing, a dry severe cough, loss of smell or “COVID toe.” Results In total, 14,408 panel members (48% male and 52% female) completed the survey. Despite overrepresentation of higher levels of education, the prevalence of obesity, smoking, diabetes and hypertension were similar to national census and health surveys. A total of 811 (5.6%) were COVID symptom-positive; highest rates were at ages 18-44 years (8.3% among), declining at older ages. Females had higher odds of reporting COVID symptoms (OR = 1.32, 95%CI 1.11 – 1.56) as did visible minorities (OR = 1.74, 1.29 – 2.35). COVID symptom positivity for respondents and their household members peaked in March (OR = 1.93, 95% CI = 1.59 – 2.34 compared to earlier months). Conclusions and Relevance This study enhances our current understanding of the progression of the COVID epidemic in Canada, with few laboratory-confirmed cases in January and February, peaking in April. The results suggest substantial viral transmission in March, before widespread testing began, and a gradual decline in cases since May.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".