London 2012 Olympic and Paralympic Games: public health surveillance and epidemiology
Bibliographic record
Abstract
Mass gatherings are regarded as potential risks for transmission of infectious diseases, and might compromise the health system of countries in which they are hosted. The evidence for increased transmission of infectious diseases at international sporting mass gatherings that attract many visitors from all over the world is not clear, and the evidence base for public health surveillance, epidemiology, and response at events such as the Olympics is small. However, infectious diseases are a recognised risk, and public health planning is, and should remain, a crucial part of the overall planning of sporting events. In this Series paper, we set out the planning and the surveillance systems that were used to monitor public health risks during the London 2012 Olympic and Paralympic Games in the summer of 2012, and draw attention to the public health issues-infectious diseases and chemical, radiation, and environmental hazards-that arose. Although the absolute risk of health-protection problems, including infectious diseases, at sporting mass gatherings is small, the need for reassurance of the absence of problems is higher than has previously been considered; this could challenge conventional public health surveillance systems. Recognition of the limitations of health-surveillance systems needs to be part of the planning for future sporting events.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".