The contribution of travellers visiting friends and relatives to notified infectious diseases in Australia: state-based enhanced surveillance
Bibliographic record
Abstract
Immigrants and their children who return to their country of origin to visit friends and relatives (VFR) are at increased risk of acquiring infectious diseases compared to other travellers. VFR travel is an important disease control issue, as one quarter of Australia's population are foreign-born and one quarter of departing Australian international travellers are visiting friends and relatives. We conducted a 1-year prospective enhanced surveillance study in New South Wales and Victoria, Australia to determine the contribution of VFR travel to notifiable diseases associated with travel, including typhoid, paratyphoid, measles, hepatitis A, hepatitis E, malaria and chikungunya. Additional data on characteristics of international travel were collected. Recent international travel was reported by 180/222 (81%) enhanced surveillance cases, including all malaria, chikungunya and paratyphoid cases. The majority of cases who acquired infections during travel were immigrant Australians (96, 53%) or their Australian-born children (43, 24%). VFR travel was reported by 117 (65%) travel-associated cases, highest for typhoid (31/32, 97%). Cases of children (aged <18 years) (86%) were more frequently VFR travellers compared to adult travellers (57%, P < 0·001). VFR travel is an important contributor to imported disease in Australia. Communicable disease control strategies targeting these travellers, such as targeted health promotion, are likely to impact importation of these travel-related infections.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".