Public health surveillance for the Toronto 2015 Pan/Parapan American Games
Bibliographic record
Abstract
BACKGROUND: Public health surveillance for previous Olympic and Paralympic Games have been described in the literature, but surveillance for regional, multisport events on a smaller scale have rarely been explored. OBJECTIVE: To describe the public health surveillance planning, implementation, results, and lessons learned from the 2015 Pan/Parapan American Games in Toronto, Ontario, Canada. INTERVENTION: Public health surveillance planning for the Games began two years in advance and involved local, provincial and federal partners, primarily focusing on infectious disease. From June to August, 2015, enhanced public health surveillance was conducted to support situational awareness and to facilitate the detection of infectious diseases and outbreaks, environmental health hazards and impacts and other major health events. OUTCOMES: No major public health incidents occurred that were associated with or a result of hosting the Games. There were two cases of reportable infectious diseases associated with the Games, and 18 public health investigations involving Games-accredited individuals (six related to vaccine-preventable diseases and 12 related to gastrointestinal illnesses or food/water safety violations). Enhanced communication mechanisms, rather than routine and syndromic surveillance systems, were the primary sources of initial notification to surveillance partners on investigations. CONCLUSION: Working with its partners, Ontario created a robust public health surveillance system for the 2015 Pan/Parapan American Games. Lessons learned, as well as the relationships and capacity developed through this experience, will be applied towards public health surveillance planning for future events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".