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Enregistrement W2099256289 · doi:10.1093/trstmh/tru051

Travel and the globalization of emerging infections

2014· editorial· en· W2099256289 sur OpenAlexaboutno aff
Poh Lian Lim

Notice bibliographique

RevueTransactions of the Royal Society of Tropical Medicine and Hygiene · 2014
Typeeditorial
Langueen
DomaineMedicine
ThématiqueTravel-related health issues
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChikungunyaOutbreakMiddle East respiratory syndromeInfluenza A virus subtype H5N1Zika virusChinaDengue feverPandemicBeijingPublic healthAir travelTravel medicineGeographyWildlife tradeMedicineGlobal healthEnvironmental healthInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)WildlifeVirologyDiseaseBiology

Résumé

récupéré en direct d'OpenAlex

Although more than 10 years have elapsed since the appearance of severe acute respiratory syndrome (SARS), this ‘first pandemic of the 21st century’ has left a lasting legacy because of its spread by travelers.1 The International Health Regulations (IHR) were re-formulated in 2005, and along with changes in mobile computing technology and social media, has led to greater transparency and faster information sharing of health events important to travel medicine and travelers.2 Emerging infections continue to be a concern for travelers with numerous events highlighted in the media and public health literature during 2013 and 2014. In 2013, there was heightened international concern over travel-associated cases of Middle East respiratory syndrome (MERS) coronavirus infections in the United Kingdom, France, Italy and countries in the Middle East.3,4 Avian influenza A (H5N1) continues to simmer in Southeast Asia with sporadic cases reported, including a recent travel-associated fatal infection of a person in Canada who had returned from Beijing, China.5 However, the situation to watch in 2014 is the rise of avian influenza A (H7N9) in China, with over 200 cases of H7N9 reported during the current 2013-2014 Northern Hemispheric winter.6 There has also been an alarming rise and spread of vector-borne infections including the large dengue outbreak in Southeast Asia in 2013, the spread of chikungunya to the Caribbean and travel-acquired Zika infections in Thailand and French Polynesia.7–9 Travelers play an important role in the spread of emerging and re-emerging infections. International travel has become more affordable as compared to 10 to 20 years ago. Global travel volume is estimated at over a billion individuals crossing an international border annually and is projected to increase. Although emerging infections remain relatively rare occurrences, these low-probability but high-impact events are another factor that travel-medicine practitioners need to be aware of, as the cumulative global risk increases. Several categories of travelers are potentially at higher risk of certain infections. Travelers visiting friends and relatives (VFR) and long-term travelers have higher exposures to vector-borne infections such as malaria, and are less likely to take health precautions before and during their residence abroad. These categories of travelers may be important groups that need to be reached with pre-travel advice.10,11 Immigrants and migrant workers who return from lower-income countries to visit friends and relatives may also introduce vector-borne infections such as chikungunya into new geographic locations that did not have prior endemic infection.12 As a clinician, how do we prepare travelers for these unpredictable events? We do know that most emerging infections have zoonotic origins and many have viral pathogens as etiologic agents. The primary routes of transmission include contact with animals, mosquito bites and respiratory exposure to infected persons. This can form the basis for advice to travelers who want to take the appropriate precautions, and provide targeted screening criteria for ill travelers returning from outbreak-affected areas. For example, travelers to China should be advised to avoid contact with poultry to minimize their risk of acquiring avian influenza, and those going to the Middle East should understand that current medical knowledge indicates that camels and bats are potential animal reservoirs or intermediaries for MERS-CoV infection.13,14 Travel is also associated with clusters of re-emerging, vaccine-preventable infections, ranging from measles to pertussis.15 Migrant populations often contain large groups of individuals who may be susceptible to infections due to a variety of reasons including socioeconomic and cultural barriers to care. Concerns about vaccines' adverse effects also lead individuals to opt out of immunizations, but travel may expose these persons to infectious risks. This presents another challenge to travel medicine clinicians who already provide advice for common health risks such as travelers' diarrhea, and clinicians must remain current on guidelines for more esoteric vaccines such as Japanese encephalitis, yellow fever or rabies. Various national or international websites such as the Centre for Disease Control and Prevention (CDC), European Centres for Disease Control (ECDC), National Travel Health Network and Centre (NaTHNAC) and WHO are of great benefit and provide resources to guide practice. Travel medicine conferences bring clinicians together with industry and academic partners to share updates on travel medicine and discuss issues of relevance. HealthMap trawls internet sources in different languages and provides near real-time ability to geo-locate disease occurrences and outbreaks.16 Ultimately, however, it is not information systems but the networks of people and astute clinicians who make sense of data, identify emerging trends and minimize the effects of emerging infections. Competing interests: None.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,003
Communication savante0,0040,004
Science ouverte0,0000,004
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0120,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,287
Écart entre enseignants0,277 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations10
Publié2014
Routes d'admission1
Résumé présentoui

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