Traveller exposures to animals: a GeoSentinel analysis
Bibliographic record
Abstract
BACKGROUND: Human coexistence with other animals can result in both intentional and unintentional contact with a variety of mammalian and non-mammalian species. International travellers are at risk for such encounters; travellers risk injury, infection and possibly death from domestic and wild animal bites, scratches, licks and other exposures. The aim of the present analysis was to understand the diversity and distribution of animal-related exposures among international travellers. METHODS: Data from January 2007 through December 2018 from the GeoSentinel Surveillance Network were reviewed. Records were included if the exposure was non-migration travel with a diagnosis of an animal (dog, cat, monkey, snake or other) bite or other exposure (non-bite); records were excluded if the region of exposure was not ascertainable or if another, unrelated acute diagnosis was reported. RESULTS: A total of 6470 animal exposures (bite or non-bite) were included. The majority (71%) occurred in Asia. Travellers to 167 countries had at least one report of an animal bite or non-bite exposure. The majority (76%) involved dogs, monkeys and cats, although a wide range of wild and domestic species were involved. Almost two-thirds (62.6%) of 4395 travellers with information available did not report a pretravel consultation with a healthcare provider. CONCLUSIONS: Minimizing bites and other animal exposures requires education (particularly during pretravel consultations) and behavioral modification. These should be supplemented by the use of pre-exposure rabies vaccination for travellers to high-risk countries (especially to those with limited access to rabies immunoglobulin), as well as encouragement of timely (in-country) post-exposure prophylaxis for rabies and Macacine alphaherpesvirus 1 (herpesvirus B) when warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".