Measles importations by international travelers, GeoSentinel 2019–2025
Bibliographic record
Abstract
BACKGROUND: The global resurgence of measles is a threat to measles elimination campaigns. Measles importations by international travelers have been identified as a risk factor for outbreaks. METHODS: We reviewed measles cases among international travelers and migrants reported to the GeoSentinel network. RESULTS: From May 2019 through June 2025, GeoSentinel recorded 53 measles cases among travelers imported into 15 different countries. Travelers of all age groups were affected, and 74 % were 21 years or older. Thirty-three travelers (61 %) were hospitalized. Seventy-nine percent of cases reported no or unknown history of vaccination against measles. CONCLUSIONS: Against a background of increasing numbers of measles cases and outbreaks globally, GeoSentinel observed a stable trend of measles importations by international travelers. Measles caused considerable morbidity among travelers. Immunization effectively prevents measles in more than 97 % of individuals. Pretravel consultations provide an important opportunity to promote vaccination coverage for all vaccine-preventable diseases, including measles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".