International travel between global urban centres vulnerable to yellow fever transmission
Bibliographic record
Abstract
OBJECTIVE: To examine the potential for international travel to spread yellow fever virus to cities around the world. METHODS: We obtained data on the international flight itineraries of travellers who departed yellow fever-endemic areas of the world in 2016 for cities either where yellow fever was endemic or which were suitable for viral transmission. Using a global ecological model of dengue virus transmission, we predicted the suitability of cities in non-endemic areas for yellow fever transmission. We obtained information on national entry requirements for yellow fever vaccination at travellers' destination cities. FINDINGS: In 2016, 45.2 million international air travellers departed from yellow fever-endemic areas of the world. Of 11.7 million travellers with destinations in 472 cities where yellow fever was not endemic but which were suitable for virus transmission, 7.7 million (65.7%) were not required to provide proof of vaccination upon arrival. Brazil, China, India, Mexico, Peru and the United States of America had the highest volumes of travellers arriving from yellow fever-endemic areas and the largest populations living in cities suitable for yellow fever transmission. CONCLUSION: Each year millions of travellers depart from yellow fever-endemic areas of the world for cities in non-endemic areas that appear suitable for viral transmission without having to provide proof of vaccination. Rapid global changes in human mobility and urbanization make it vital for countries to re-examine their vaccination policies and practices to prevent urban yellow fever epidemics.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".