Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".