Clustering of and Risk Factors for the Porcine High Fever Disease in a Region of Vietnam
Bibliographic record
Abstract
Porcine high fever disease (PHFD) emerged in 2006 in China and spread to Vietnam. Little work has been carried out to investigate PHFD risk factors and space-time dynamics. To fill this gap, we investigated probable cases of PHFD at household level as the outcome. A study area, approximately 100 sq. km, was selected from a province of southern Vietnam that had reported the outbreak of PHFD in 2008. A survey was conducted in the study area to collect information about swine health problems during 2008. The questionnaire included three sections: general information, clinical signs of disease in pigs and production factors believed to be risk factors. Cases were defined at the household level and included interpretation of clinical signs in series. Logistic regression with a random intercept at the hamlet level was used to assess risk factors for PHFD at the household level. Spatial clustering was investigated using the D-function and a Cuzick-Edward's test. Spatial clusters were evaluated using a spatial relative risk surface and the spatial scan statistic using a Bernoulli model. Space-time clustering was explored using a space-time K-function and Knox's test. Space-time clusters were evaluated using a space-time permutation model in SaTScan. Of 955 households with questionnaire data, 33.4% were classified as cases. The statistical significance of space and space-time clustering differed between methods employed. The risk factors associated with occurrence of cases were higher numbers of sows and finishing pigs (log 2 transformed), receiving pigs from an external source and the interaction between using 'water green crop' (WGC) as pig feed and owning ducks with or without direct contact with pigs. The interaction between the presence of ducks and feeding WGC to pigs suggested the involvement of pathogens that might be present in water (environment) and could further replicate in or on ducks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".