GeoSentinel: past, present and future
Bibliographic record
Abstract
RATIONALE FOR REVIEW: In response to increased concerns about emerging infectious diseases, GeoSentinel, the Global Surveillance Network of the International Society of Travel Medicine in partnership with the US Centers for Disease Control and Prevention (CDC), was established in 1995 in order to serve as a global provider-based emerging infections sentinel network, conduct surveillance for travel-related infections and communicate and assist global public health responses. This review summarizes the history, past achievements and future directions of the GeoSentinel Network. KEY FINDINGS: Funded by the US CDC in 1996, GeoSentinel has grown from a group of eight US-based travel and tropical medicine centers to a global network, which currently consists of 68 sites in 28 countries. GeoSentinel has provided important contributions that have enhanced the ability to use destination-specific differences to guide diagnosis and treatment of returning travelers, migrants and refugees. During the last two decades, GeoSentinel has identified a number of sentinel infectious disease events including previously unrecognized outbreaks and occurrence of diseases in locations thought not to harbor certain infectious agents. GeoSentinel has also provided useful insight into illnesses affecting different traveling populations such as migrants, business travelers and students, while characterizing in greater detail the epidemiology of infectious diseases such as typhoid fever, leishmaniasis and Zika virus disease. CONCLUSIONS: Surveillance of travel- and migration-related infectious diseases has been the main focus of GeoSentinel for the last 25 years. However, GeoSentinel is now evolving into a network that will conduct both research and surveillance. The large number of participating sites and excellent geographic coverage for identification of both common and illnesses in individuals who have traversed international borders uniquely position GeoSentinel to make important contributions of travel-related infectious diseases in the years to come.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".