Travel-related infections presenting in Europe: A 20-year analysis of EuroTravNet surveillance data
Bibliographic record
Abstract
BACKGROUND: Disease epidemiology of (re-)emerging infectious diseases is changing rapidly, rendering surveillance of travel-associated illness important. METHODS: We evaluated travel-related illness encountered at EuroTravNet clinics, the European surveillance sub-network of GeoSentinel, between March 1, 1998 and March 31, 2018. FINDINGS: 103,739 ill travellers were evaluated, including 11,239 (10.8%) migrants, 89,620 (86.4%) patients seen post-travel, and 2,880 (2.8%) during and after travel. Despite increasing numbers of patient encounters over 20 years, the regions of exposure by year of clinic visits have remained stable. In 5-year increments, greater proportions of patients were migrants or visiting friends and relatives (VFR); business travel-associated illness remained stable; tourism-related illness decreased. Falciparum malaria was amongst the most-frequently diagnosed illnesses with 5,254 cases (5.1% of all patients) and the most-frequent cause of death (risk ratio versus all other illnesses 2.5:1). Animal exposures requiring rabies post-exposure prophylaxis increased from 0.7% (1998-2002) to 3.6% (2013-2018). The proportion of patients with seasonal influenza increased from zero in 1998-2002 to 0.9% in 2013-2018. There were 44 cases of viral haemorrhagic fever, most during the past five years. Arboviral infection numbers increased significantly as did the range of presenting arboviral diseases, dengue and chikungunya diagnoses increased by 2.6% and 1%, respectively. INTERPRETATION: Travel medicine must adapt to serve the changing profile of travellers, with an increase in migrants and persons visiting relatives and friends and the strong emergence of vector-borne diseases, with potential for further local transmission in Europe. FUNDING: This project was supported by a cooperative agreement (U50CK00189) between the Centers for Disease Control and Prevention to the International Society of Travel Medicine (ISTM) and funding from the ISTM and the Public Health Agency of Canada.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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".