Zika beyond the Americas: Travelers as sentinels of Zika virus transmission. A GeoSentinel analysis, 2012 to 2016.
Bibliographic record
Abstract
BACKGROUND: Zika virus (ZIKV) was first isolated in Africa; decades later, caused large outbreaks in the Pacific, and is considered endemic in Asia. We aim to describe ZIKV disease epidemiology outside the Americas, the importance of travelers as sentinels of disease transmission, and discrepancies in travel advisories from major international health organizations. METHODS AND FINDINGS: This descriptive analysis using GeoSentinel Surveillance Network records involves sixty-four travel and tropical medicine clinics in 29 countries. Ill returned travelers with a confirmed or probable diagnosis of ZIKV disease acquired in Africa, Asia and the Pacific seen between 1 January 2012 and 31 December 2016 are included, and the frequencies of demographic, trip, and diagnostic characteristics described. ZIKV was acquired in Asia (18), the Pacific (10) and Africa (1). For five countries (Indonesia, Philippines, Thailand, Vietnam, Cameroon), GeoSentinel patients were sentinel markers of recent Zika activity. Additionally, the first confirmed ZIKV infection acquired in Kiribati was reported to GeoSentinel (2015), and a probable case was reported from Timor Leste (April 2016), representing the only case known to date. Review of Zika situation updates from major international health authorities for country risk classifications shows heterogeneity in ZIKV country travel advisories. CONCLUSIONS: Travelers are integral to the global spread of ZIKV, serving as sentinel markers of disease activity. Although GeoSentinel data are collected by specialized clinics and do not capture all imported cases, we show that surveillance of imported infections by returned travelers augments local surveillance system data regarding ZIKV epidemiology and can assist with risk categorization by international authorities. However, travel advisories are variable due to risk uncertainties.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".