Malaria in Travelers: A Review of the GeoSentinel Surveillance Network
Bibliographic record
Abstract
BACKGROUND: Malaria is a common and important infection in travelers. METHODS: We have examined data reported to the GeoSentinel surveillance network to highlight characteristics of malaria in travelers. RESULTS: A total of 1140 malaria cases were reported (60% of cases were due to Plasmodium falciparum, 24% were due to Plasmodium vivax). Male subjects constituted 69% of the study population. The median duration of travel was 34 days; however, 37% of subjects had a travel duration of < or =4 weeks. The majority of travellers did not have a pretravel encounter with a health care provider. Most cases occurred in travelers (39%) or immigrants/refugees (38%). The most common reasons for travel were to visit friends/relatives (35%) or for tourism (26%). Three-quarters of infections were acquired in sub-Saharan Africa. Severe and/or complicated malaria occurred in 33 cases, with 3 deaths. Compared with others in the GeoSentinel database, patients with malaria had traveled to sub-Saharan Africa more often, were more commonly visiting friends/relatives, had traveled for longer periods, presented sooner after return, were more likely to have a fever at presentation, and were less likely to have had a pretravel encounter. In contrast to immigrants and visitors of friends or relatives, a higher proportion (73%) of the missionary/volunteer group who developed malaria had a pretravel encounter with a health care provider. Travel to sub-Saharan Africa and Oceania was associated with the greatest relative risk of acquiring malaria. CONCLUSIONS: We have used a global database to identify patient and travel characteristics associated with malaria acquisition and characterized differences in patient type, destinations visited, travel duration, and malaria species acquired.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".