Intervention strategies to reduce the risk of zoonotic infection with avian influenza viruses: scientific basis, challenges and knowledge gaps
Bibliographic record
Abstract
A range of measures has been recommended and used for the control and prevention of avian influenza. These measures are based on the assessment of local epidemiological situations, field observations and other scientific information. Other non-technical factors are (or in some cases should be) taken into account when developing and recommending control measures. The precise effects under field conditions of most individual interventions applied to control and prevent avian influenza have not been established or subjected to critical review, often because a number of measures are applied simultaneously without controls. In most cases, the combination of measures used results in control or elimination of the virus although there are some countries where this has not been the case. In others, especially those with low poultry density, it is not clear whether the link between the adoption of a set of measures and the subsequent control of the disease is causative. This article discusses the various measures recommended, with particular emphasis on stamping out and vaccination, examines how these measures assist in preventing zoonotic infections with avian influenza viruses and explores gaps in knowledge regarding their effectiveness.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".