Chikungunya infection in returned travellers: results from the GeoSentinel network, 2005–2020
Bibliographic record
Abstract
BACKGROUND: Chikungunya is an important travel-related disease because of its rapid geographical expansion and potential for prolonged morbidity. Improved understanding of the epidemiology of travel-related chikungunya infections may influence prevention strategies including education and vaccination. METHODS: We analysed data from travellers with confirmed or probable chikungunya reported to GeoSentinel sites from 2005 to 2020. Confirmed chikungunya was defined as a compatible clinical history plus either virus isolation, positive nucleic acid test or seroconversion/rising titre in paired sera. Probable chikungunya was defined as a compatible clinical history with a single positive serology result. RESULTS: 1202 travellers (896 confirmed and 306 probable) with chikungunya were included. The median age was 43 years (range 0-91; interquartile range [IQR]: 31-55); 707 (58.8%) travellers were female. Most infections were acquired in the Caribbean (28.8%), Southeast Asia (22.8%), South Central Asia (14.2%) and South America (14.2%). The highest numbers of chikungunya cases reported to GeoSentinel were in 2014 (28.3%), 2015 (14.3%) and 2019 (11.9%). The most frequent reasons for travel were tourism (n = 592; 49.3%) and visiting friends or relatives (n = 334; 27.7%). The median time to presentation to a GeoSentinel site was 23 days (IQR: 7-52) after symptom onset. In travellers with confirmed chikungunya and no other reported illnesses, the most frequently reported symptoms included musculoskeletal symptoms (98.8%), fever/chills/sweats (68.7%) and dermatologic symptoms (35.5%). Among 917 travellers with information available, 296 (32.3%) had a pretravel consultation. CONCLUSIONS: Chikungunya was acquired by international travellers in almost 100 destinations globally. Vector precautions and vaccination where recommended should be integrated into pretravel visits for travellers going to areas with chikungunya or areas with the potential for transmission. Continued surveillance of travel-related chikungunya may help public health officials and clinicians limit the transmission of this potentially debilitating disease by defining regions where protective measures (e.g. pretravel vaccination) should be strongly considered.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".