Sentinel Surveillance in Travel Medicine: 20 Years of GeoSentinel Publications (1999–2018)
Bibliographic record
Abstract
A recent comprehensive literature review highlighted that between 6% and 87% of travellers become ill during or as a result of their travel.1 In this issue of the Journal of Travel Medicine, the value of sentinel surveillance in international travellers to identify and describe rare medical problems such as mycoses was highlighted.2 In the time period from 1997 through 2017, 61 cases of mycoses were identified out of more than 60 000 included case records reported to GeoSentinel. GeoSentinel is a global surveillance network now consisting of 70 travel and tropical medicine centres situated in 31 countries across 6 continents. Although sentinel surveillance in individual travel medicine clinics is helpful,3 rare diseases such as mycoses, or emerging, and other novel epidemiological features of infectious diseases in travellers can only be studied through a global surveillance system of returning travellers such as GeoSentinel. GeoSentinel was founded in 1995 by the International Society of Travel Medicine (ISTM) and is supported by ISTM the US Centers for Disease Control and Prevention and the Public Health Agency of Canada. GeoSentinel is based on the concept that travel medicine providers who encounter returning travellers are ideally situated to detect geographic and temporal trends in morbidity among travellers, and that by reporting such data centrally, such trends will be detected with greater frequency and expedience. Much of our knowledge on health problems and infections encountered by international travellers has evolved as a result of such sentinel surveillance.4 As travellers serve as a vehicle for the spread of diseases,5 the initial intent of GeoSentinel as a provider-based sentinel network was to track emerging infections at their point of entry, for example, influenza,6 or to identify outbreaks that may otherwise have gone unnoticed such as the outbreak of leptospirosis in a sporting event that involved many international visitors.7 The scope has broadened over time to monitor global trends in disease occurrence among travellers;8 to determine travel destinations with the highest risk exposure;9 to ascertain risk factors and morbidity in groups of travellers categorised by travel purpose and type of traveller;10 and to describe specific diseases,11 including those that are of extreme public health importance.12,13 GeoSentinel is now a worldwide communication and data collection network for the surveillance of travel-related morbidity, the largest of its kind. Limitations of the network include the absence of denominator data precluding the estimation of relative risk of specific travel-acquired illnesses; regional variation in the availability and application of microbiological diagnostics and therapeutics; lack of full clinical linkage of records, which limits the scope of data collected to basic demographic and travel data; localisation of sites to predominantly ambulatory referral-based centres staffed by specialists in travel and tropical medicine, which translates to underrepresentation in the network of paediatric and hospitalised cases, as well as mild self-limited illnesses or those with either very short (e.g. influenza) or very long (e.g. hepatitis B) incubation periods. CanTravNet is also an initiative of the International Society of Travel Medicine (ISTM), in collaboration with the Public Health Agency of Canada (PHAC), founded in 2012. All core sites are members of the GeoSentinel Global Surveillance Network. EuroTravNet was first funded by the European Centre for Disease Prevention and Control (ECDC). It was funded by ECDC from 2008 to 2012 and the ISTM. EuroTravNet is now funded by the ISTM and the Institutes Hospitalo-Universitaire (IHU) Méditerranée Infection Foundation in Marseille. The EuroTravNet founding core sites and members all belong to the GeoSentinel Global Surveillance Network. Given the substantial efforts towards tracking, defining and analysing infectious diseases trends amongst travellers and migrants over the past two decades, it is timely to highlight the publications stemming from GeoSentinel sentinel surveillance. Table 1 summarises the 99 peer-reviewed publications arising from network-wide (global) analyses of GeoSentinel data as well as publications arising from the two regional networks of GeoSentinel, EuroTravNet and CanTravNet, from 1999 to 2018. Such knowledge products are a testament to the wide scope of GeoSentinel activities, the global collaborative nature of its leaders, the breadth of the research ranging from high-level epidemiology of illness in travellers to specific travel-acquired diseases such as dengue and malaria, and its impact. Publications arising from analysis of GeoSentinel Surveillance Network Data Publications arising from analysis of GeoSentinel Surveillance Network Data Given the recognised globalisation of infectious diseases and the high degree of mobility of populations, vigilance to combat threats to local, national and global public health is imperative. To this end, continued sentinel surveillance for new and emerging infectious diseases, as well as detection of existing pathogens in novel epidemiologic niches, is imperative. Conflict of interest: None declared.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".