Canadian COVID-19 Outbreak Surveillance System: implementation of national surveillance during a global pandemic
Bibliographic record
Abstract
SETTING: Early in the SARS-CoV-2 pandemic, the need to develop systematic outbreak surveillance at the national level to monitor trends in SARS-CoV-2 outbreaks was identified as a priority for the Public Health Agency of Canada (PHAC). The Canadian COVID-19 Outbreak Surveillance System (CCOSS) was established to monitor the frequency and severity of SARS-CoV-2 outbreaks across various community settings. INTERVENTION: PHAC engaged with provincial/territorial partners in May 2020 to develop goals and key data elements for CCOSS. In January 2021, provincial/territorial partners began submitting cumulative outbreak line lists on a weekly basis. OUTCOMES: Eight provincial and territorial partners, representing 93% of the population, submit outbreak data on the number of cases and severity indicators (hospitalizations and deaths) for 24 outbreak settings to CCOSS. Outbreak data can be integrated with national case data to supply information on case demographics, clinical outcomes, vaccination status, and variant lineages. Data aggregated to the national level are used to conduct analyses and report on outbreak trends. Evidence from CCOSS analyses has been useful in supporting provincial/territorial outbreak investigations, informing policy recommendations, and monitoring the impact of public health measures (vaccination, closures) in specific outbreak settings. IMPLICATIONS: The development of a SARS-CoV-2 outbreak surveillance system complemented case-based surveillance and furthered the understanding of epidemiological trends. Further efforts are required to better understand SARS-CoV-2 outbreaks for Indigenous populations and other priority populations, as well as create linkages between genomic and epidemiological data. As SARS-CoV-2 outbreak surveillance enhanced case surveillance, outbreak surveillance should be a priority for emerging public health threats.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".