Spatial analysis of bluetongue cases and vaccination of Swiss cattle in 2008 and 2009
Bibliographic record
Abstract
Bluetongue (BT) is a vector-borne viral disease of ruminants. The infection is widespread globally with major implications for international animal trade and production. In 2006, BT virus serotype 8 (BTV-8) was encountered in Europe for the first time, causing extensive production losses and death in susceptible livestock. Following the appearance of BTV-8 in Switzerland in 2007, a compulsory vaccination programme was launched in the subsequent year. Due to social factors and difficulties to reach animals on high pasture, the regional vaccination coverage varied across the country in both 2008 and 2009. In this study, the effect of vaccination on the spatial occurrence of BTV-8 and the associated relative disease risk in Switzerland in 2008 and 2009 were investigated by a spatial Bayesian hierarchical approach. Bayesian posterior distributions were obtained by integrated nested Laplace approximations, a promising alternative to commonly used Markov chain Monte Carlo methods. The number of observed BTV-8 outbreaks in Switzerland decreased notably from 2008 to 2009. However, only a non-significant association between vaccination coverage and the probability of a spatial unit being infected with BTV-8 was identified using the model developed for this study. The relative disease risk varied significantly across the country, with a higher relative risk of BTV-8 infection in western and north-western Switzerland where environmental conditions are more suitable for vector presence and viral transmission. Examination of the spatial correlation between disease occurrence, control measures and associated ecological factors can be valuable in the evaluation and development of disease control programmes, allowing prioritisation of areas with a high relative risk of disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".