Bordetella pertussis infections in travelers: data from the GeoSentinel global network
Bibliographic record
Abstract
Background: Pertussis is a highly contagious, vaccine-preventable respiratory infection that is endemic worldwide. There are limited data regarding the occurrence of pertussis in travelers. The objective of this study is to identify travel-related pertussis cases reported to the GeoSentinel Surveillance Network. Methods: This is a descriptive, retrospective analysis of GeoSentinel records from 25 travel/tropical medicine clinics in 16 countries. Frequencies of demographic and travel-related characteristics and symptoms of 74 cases of pertussis in travelers and new immigrants from 1999 to 2015 were analysed. Results: There were 74 probable and confirmed cases of pertussis in the GeoSentinel database; median age was 44 years, and 38 (51%) patients were female. Tourism was the most common reason for travel (41; 55%). Country of exposure was determined in 66 cases with travelers returning from India and China constituting the highest number of cases (10 cases each; 15% each). Seventy of 74 (95%) patients had respiratory symptoms, while fatigue and fever were reported by 21 (28%) and 20 (27%), respectively. Immunization status against pertussis was unknown. Most cases were reported after 2005 (69; 93%). Conclusions: Our study describes 74 cases of pertussis acquired during travel and reported to the GeoSentinel Network. Pertussis should be considered in returned travelers who present with respiratory symptoms. Surveillance and detection of imported cases are important to prevent onward transmission in the community. The pre-travel consultation provides an opportunity to verify immunization status and provide routine vaccinations such as pertussis.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".