A public health enhanced surveillance system for a mass gathering event
Bibliographic record
Abstract
BACKGROUND: From June 7 to June 9, 2018, a G7 Summit was held in the Canadian province of Quebec. This international political mass gathering event posed a number of potential risks to public health. OBJECTIVE: To assess three additional monitoring strategies to detect public health threats during a mass gathering event. INTERVENTION: In addition to routine public health monitoring, a partnership was created and three monitoring strategies were put in place three days before, during and six days after the G7 event: the analysis of data on the presenting complaint and discharge diagnosis from 11 emergency departments in the area using the logical Early Aberration Reporting System; the daily polling of key health partners with an online questionnaire; and the analysis of calls to Info-Santé, a government-run telephone consultation service for the public regarding health and social issues. RESULTS: Emergency room data produced 78 alerts from the presenting complaints and 39 alerts from the discharge diagnoses. Of these 117 alerts, two were investigated (one in the respiratory and one in the neurological-muscular categories) and no other interventions were required. With a few exceptions, all of the health partners completed the online survey each day and no signal of concern was generated. Compared with historical data, no increase or differences in calls to Info-Santé were detected during the monitoring period. CONCLUSION: The three additional monitoring strategies developed to detect events of public health importance during the 2018 G7 Summit in Quebec were successful in gathering timely data for analysis. Close collaboration and good participation from the different partners were essential to this project. However, because no public health event occurred, it was not possible to determine whether the enhanced surveillance system had sufficient speed and sensitivity for timely detection and response.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".