Zika among international travellers presenting to GeoSentinel sites, 2012–2019: implications for clinical practice
Bibliographic record
Abstract
INTRODUCTION: International travellers contribute to the rapid spread of Zika virus (ZIKV) and its sentinel identification globally. We describe ZIKV infections among international travellers seen at GeoSentinel sites with a focus on ZIKV acquired in the Americas and the Caribbean, describe countries of exposure and traveller characteristics, and assess ZIKV diagnostic testing by site. METHODS: Records with an international travel-related diagnosis of confirmed or probable ZIKV from January 2012 through December 2019 reported to GeoSentinel with a recorded illness onset date were included to show reported cases over time. Records from March 2016 through December 2019 with an exposure region of the Americas or the Caribbean were included in the descriptive analysis. A survey was conducted to assess the availability, accessibility and utilization of ZIKV diagnostic tests at GeoSentinel sites. RESULTS: GeoSentinel sites reported 525 ZIKV cases from 2012 through 2019. Between 2012 and 2014, eight cases were reported, and all were acquired in Asia or Oceania. After 2014, most cases were acquired in the Americas or the Caribbean, a large decline in ZIKV cases occurred in 2018-19.Between March 2016 and December 2019, 423 patients acquired ZIKV in the Americas or the Caribbean, peak reporting to these regions occurred in 2016 [330 cases (78%)]. The median age was 36 years (range: 3-92); 63% were female. The most frequent region of exposure was the Caribbean (60%). Thirteen travellers were pregnant during or after travel; one had a sexually acquired ZIKV infection. There was one case of fetal anomaly and two travellers with Guillain-Barré syndrome. GeoSentinel sites reported various challenges to diagnose ZIKV effectively. CONCLUSION: ZIKV should remain a consideration for travellers returning from areas with risk of ZIKV transmission. Travellers should discuss their travel plans with their healthcare providers to ensure ZIKV prevention measures are taken.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".