COVID-19 outbreak trends in Canada, 2021
Bibliographic record
Abstract
Background: In January 2021, the Public Health Agency of Canada launched an outbreak surveillance system, the Canadian COVID-19 Outbreak Surveillance System (CCOSS), with the goal of monitoring incidence and severity of coronavirus disease 2019 (COVID-19) outbreaks across various community settings and complementing case surveillance. Methods: Seven provinces were included in this report; these provinces submitted weekly cumulative COVID-19 outbreak line lists to CCOSS in 2021. Data includes administrative variables (e.g. date outbreak declared, date outbreak declared over, outbreak identifier), 24 outbreak settings, and number of confirmed cases and outcomes (hospitalization, death). Descriptive analyses for COVID-19 outbreaks across Canada from January 3, 2021, to January 1, 2022, were performed examining trends over time, severity, and outbreak size. Results: Incidence of outbreaks followed similar trends to case incidence. Outbreaks were most common in school and childcare settings (39%) and industrial/agricultural settings (21%). Outbreak size ranged from 2 to 639 cases per outbreak; the median size was four cases per outbreak. Correctional facilities had the largest median outbreak size with 18 cases per outbreak, followed by long-term care facilities with 10 cases per outbreak. During periods of high case incidence, outbreaks may be under-ascertained due to limited public health capacity, or reporting may be biased towards high-risk settings prioritized for testing. Outbreaks reported to CCOSS were dominated by jurisdictions with the largest populations. Conclusion: The trends illustrate that COVID-19 outbreaks in 2021 were reported most frequently in community settings such as schools; however, the largest outbreaks occurred in congregate living settings. The information gathered from outbreak surveillance complemented case incidence trends and furthered understanding of COVID-19 in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".