An Epidemiological Analysis of the Foot-and-Mouth Disease Epidemic in Miyazaki, Japan, 2010
Bibliographic record
Abstract
An epidemic of foot-and-mouth disease occurred in Miyazaki, Japan, beginning in late March 2010. Here, we document the descriptive epidemiological features and investigate the between-farm transmission dynamics. As of 10 July 2010, a total of 292 infected premises have been confirmed with a cumulative incidence for cattle and pig herds of 8.5% and 36.4%, respectively, for the whole of Miyazaki prefecture. Pig herds were more likely to be infected than cattle herds (odds ratio = 4.3 [95% confidence interval (CI): 3.2, 5.7]). Modelling analysis suggested that the relative susceptibility of a cattle herd is 4.2 times greater than a typical pig herd (95% CI: 3.9, 4.5), while the relative infectiousness of a pig herd is estimated to be 8.0 times higher than a cattle herd (95% CI: 5.0, 13.6). The epidemic peak occurred around mid-May, after which the incidence started to decline and the effective reproduction numbers from late May were mostly less than unity, although a vaccination programme in late May could have masked symptoms in infected animals. The infected premises were geographically confined to limited areas in Miyazaki, but sporadic long-distance transmissions were seen within the prefecture. Given that multiple outbreaks in Far East Asian countries have occurred since early 2010, continued monitoring and surveillance is deemed essential.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".