Investigation of travel-related cases in a multinational outbreak: example of the Shiga-toxin producing<i>E. coli</i>outbreak in Germany, May–June 2011
Bibliographic record
Abstract
Early investigation of travel-related cases in an outbreak of an emerging infectious disease can provide useful information to epidemiologists to characterize the exposure, while they may differ in demographic profiles from cases reported in the country where the outbreak has occurred. During the spring 2011 E. coli outbreak in Germany, we proposed a methodological approach to collect a minimal set of demographic and clinical data that are relatively easy to obtain and available at an early stage of an outbreak investigation. Ninety-eight STEC O104 travel-related cases were reported in a survey by seven EU countries, Switzerland, Canada and the USA. We found a mean incubation period (n = 50) of 8·5 days, which confirmed previous estimations communicated by the Robert Koch Institute. No significant association was found between the duration of the incubation period and possible demographic and clinical factors, although the older the age, the shorter the incubation period that was observed. Such approach and observations are informative for further investigations of outbreaks of enterohaemorrhagic E. coli or other emerging infectious diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".