Global trends in air travel: implications for connectivity and resilience to infectious disease threats
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Increased connectivity via air travel can facilitate the geographic spread of infectious diseases. The number of travellers alone does not explain risk; passenger origin and destination will also influence risk of disease introduction and spread. We described trends in international air passenger numbers and connectivity between countries with different capacities to detect and respond to infectious disease threats. METHODS: We used the Fragile States Index (FSI) as an annual measure of country-level resilience and capacity to respond to infectious disease events. Countries are categorized as: Sustainable, Stable, Warning or Alert, in order of increasing fragility. We included data for 177 sovereign states for the years 2010 to 2019. Annual inbound and outbound international air passengers for each country were obtained for the same time period. We examined trends in FSI score, trends in worldwide air travel and the association between a state's FSI score and air travel. RESULTS: Among countries included in the FSI rankings, the total number of outbound passengers increased from 0.865 billion to 1.58 billion between 2010 and 2019. Increasing fragility was associated with a decrease in travel volumes, with a 2.5% (95% CI: 2.0-3.1%) reduction in passengers per 1-unit increase in FSI score. Overall, travel between countries of different FSI categories either increased or remained stable. CONCLUSIONS: The world's connectivity via air travel has increased dramatically over the past decade. There has been notable growth in travel from Warning and Stable countries, which comprise more than three-quarters of international air travel passengers. These countries may have suboptimal capacity to detect and respond to infectious disease threats that emerge within their borders.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 it