Association between air travel and importation of chikungunya into the USA
Bibliographic record
Abstract
Background: During infectious disease outbreaks with pandemic potential, the number of air passengers travelling from the outbreak source to international destinations has been used as a proxy for disease importation risk to new locations. However, evaluations of the validity of this approach are limited. We sought to quantify the association between international air travel and disease importation using the 2014-2016 chikungunya outbreak in the Americas as a case study. Methods: We used country-level chikungunya case data to define a time period of epidemic activity for each of the 45 countries and territories in the Americas reporting outbreaks between 2014 and 2016. For each country, we identified airports within or proximate to areas considered suitable for chikungunya transmission and summed the number of commercial air passengers departing from these airports during the epidemic period to each US state. We used negative binomial models to quantify the association between the number of incoming air passengers from countries experiencing chikungunya epidemics and the annual rate of chikungunya importation into the USA at the state level. Results: We found a statistically significant positive association between passenger flows via airline travel from countries experiencing chikungunya epidemics and the number of imported cases in the USA at the state level (P < 0.0001). Additionally, we found that as the number of arriving airline passengers increased by 10%, the estimated number of imported cases increased by 5.2% (95% CI: 3.0-7.6). Conclusion: This validation study demonstrated that air travel was strongly associated with observed importation of chikungunya cases in the USA and can be a useful proxy for identifying areas at increased risk for disease importation. This approach may be useful for understanding exportation risk of other arboviruses.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".