Estimating Air Travel–Associated Importations of Dengue Virus Into Italy
Bibliographic record
Abstract
BACKGROUND: Southern Europe is increasingly at risk for dengue emergence, given the seasonal presence of relevant mosquito vectors and suitable climatic conditions. For example, Aedes mosquitoes, the main vector for both dengue and chikungunya, are abundant in Italy, and Italy experienced the first ever outbreak of chikungunya in Europe in 2007. We set out to estimate the extent of dengue virus importations into Italy via air travelers. METHODS: We attempted to quantify the number of dengue virus importations based on modeling of published estimates on dengue incidence in the countries of disembarkation and analysis of data on comprehensive air travel from these countries into Italy's largest international airport in Rome. RESULTS: From 2005 to 2012, more than 7.3 million air passengers departing from 100 dengue-endemic countries arrived in Rome. Our Importation Model, which included air traveler volume, estimated the incidence of dengue infections in the countries of disembarkation, and the probability of infection coinciding with travel accounted for an average of 2,320 (1,621-3,255) imported dengue virus infections per year, of which 572 (381-858) were "apparent" dengue infections and 1,747 (1,240-2,397) "inapparent." CONCLUSIONS: Between 2005 and 2012, we found an increasing trend of dengue virus infections imported into Rome via air travel, which may pose a potential threat for future emergence of dengue in Italy, given that the reoccurring pattern of peak importations corresponds seasonally with periods of relevant mosquito vector activity. The observed increasing annual trends of dengue importation and the consistent peaks in late summer underpin the urgency in determining the threshold levels for the vector and infected human populations that could facilitate novel autochthonous transmission of dengue in Europe.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".