Incubation period for campylobacteriosis and its importance in the estimation of incidence related to travel
Bibliographic record
Abstract
Differentiation between travel-related and domestic cases of infectious disease is important in managing risk. Incubation periods of cases from several outbreaks of campylobacteriosis in Canada, Europe, and the United States with defined exposure time of less than 24 hours were collated to provide information on the incubation period distribution. This distribution was consistent across the varied outbreaks considered, with 84% (702/832) of cases having an incubation period of four days or less and 1% having an incubation period of eight days or more. The incubation period distribution was incorporated into a model for the number of travel-related cases presenting with symptom onset at given dates after return to their country of residence. Using New Zealand notification data between 2006 and 2010 for cases who had undertaken foreign travel within 10 days prior to symptom onset, we found that 29.6% (67/227 cases; 95% confidence interval (CI): 28.3–30.8%) of these cases were likely to have been domestic cases. When cases with symptom onset prior to arrival were included, the probable domestic cases represented 11.8% (67/571; 95% CI: 11.2–12.3%). Consideration of incubation time distributions and consistent collection of travel start/end dates with symptom onset dates would assist attribution of cases to foreign travel.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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".