Travellers and influenza: risks and prevention
Bibliographic record
Abstract
BACKGROUND: Influenza viruses are among the major causes of serious human respiratory tract infection worldwide. In line with the high disease burden attributable to influenza, these viruses play an important, but often neglected, role in travel medicine. Guidelines and recommendations regarding prevention and management of influenza in travellers are scarce. Of special interest for travel medicine are risk populations and also circumstances that facilitate influenza virus transmission and spread, like travel by airplane or cruise ship and mass gatherings. METHODS: We conducted a PUBMED/MEDLINE search for a combination of the MeSH terms Influenza virus, travel, mass gathering, large scale events and cruise ship. In addition we gathered guidelines and recommendations from selected countries and regarding influenza prevention and management in travellers. By reviewing these search results in the light of published knowledge in the fields of influenza prevention and management, we present best practice advice for the prevention and management of influenza in travel medicine. RESULTS: Seasonal influenza is among the most prevalent infectious diseases in travellers. Known host-associated risk factors include extremes of age and being immune-compromised, while the most relevant environmental factors are associated with holiday cruises and mass gatherings. CONCLUSIONS: Pre-travel advice should address influenza and its prevention for travellers, whenever appropriate on the basis of the epidemiological situation concerned. Preventative measures should be strongly recommended for travellers at high-risk for developing complications. In addition, seasonal influenza vaccination should be considered for any traveller wishing to reduce the risk of incapacitation, particularly cruise ship crew and passengers, as well as those participating in mass gatherings. Besides advice concerning preventive measures and vaccination, advice on the use of antivirals may be considered for some travellers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".