Preparing for infectious disease threats at mass gatherings: the case of the Vancouver 2010 Olympic Winter Games
Bibliographic record
Abstract
ith the global population approaching seven billion and international access to commercial air travel expanding, the number, frequency and scale of human congregations has increased dramatically during the past half century. Today, mass gatherings of hundreds of thousands to millions of people from all corners of the globe have become common. Such gatherings are held for a multitude of reasons: religious (e.g., the Hajj), political (e.g., Group of 20 [G-20] summits), socio-cultural (e.g., World Pride) and sports-related (e.g., Olympic Games), to name a few. 1 Despite their importance, mass gatherings carry the risk of locally amplifying and subsequently disseminating infectious disease threats around the world. 2 When travellers attend a mass gathering, they may unknowingly introduce infectious agents acquired in their home environments. In settings conducive to the spread of infection, epidemics among attendees and their contacts may ensue. Those who are exposed may subsequently transport the infectious agents internationally, spawning new epidemics in other parts of the world. urrent efforts to prepare for infectious disease threats at mass gatherings are generally led by the host country, often in collaboration with international public health agencies. 2 Such efforts employ strategies that are directed, for the most part, at the site of the gathering. These may include enhanced surveillance, infection control measures to minimize the transmission of disease and ensuring the availability of resources to enable rapid response to an epidemic, should one arise. However, these efforts do not typically take into account a broader understanding of the populations attending the mass gathering and the risks associated with infectious disease threats at their points of departure. We propose a conceptual model that could complement existing preparedness efforts by expanding the geographic perspective of public health surveillance worldwide at a time when large numbers of people from around the globe are travelling to attend a mass gathering. Using this model would provide public health experts with the opportunity to identify and deal with an infectious disease threat at its source and, failing that, at the site of the mass gathering. This approach could potentially prevent importation of infection by persons travelling to the site of the mass gathering and/or exportation of infection as attendees at the gathering return home.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".