Modelling for assessing the compulsory vaccination strategy against highly pathogenic avian influenza in a poultry farm of China
Bibliographic record
Abstract
• Data-driven modelling for the transmission of HPAI in a poultry farm. • China mandatory vaccination plan effectively controls HPAI spread in poultry. • High virus transmissibility increases flock losses despite vaccination. • Comprehensive post-vaccination measures needed for optimal HPAI control. • Regular monitoring, vaccine evaluation, and biosecurity upgrades recommended. The prevalence of highly pathogenic avian influenza (HPAI) has become substantial obstacles for the poultry industry and international trade with potential threat to human health. We developed a data-driven transmission model for quantitatively assessing the risk of HPAI spread in a henhouse under the China national mandatory vaccination plan for prevention and control of HPAI in the poultry industry. With data from farm investigations and literature reviews, we calibrated our model to estimate some key model parameters. We employed the Latin Hypercubic Sampling (LHS) technique for multiple sampling during simulations and subsequently conducted statistical analysis to enhance the accuracy and reliability of our results. We found that within a designated single henhouse, regardless of whether the initial virus level is 5 or 500 times the minimum viral load required to trigger highly fatal avian influenza, when the virus transmission capacity is low (the basic reproductive number R 0 ∈ ( 2 , 3 ) ), implementing mandatory vaccination measures according to the policy-stipulated timings will keep the average overall mortality rate of chickens with maternal antibodies within the range of 0.08 %-0.42 %. However, if the virus transmission capacity doubles, the loss of chickens will increase by 3–5 times, but it will still be significantly lower than the normal mortality rate (5 %). Therefore, to optimize post-vaccination management and control, we recommend monitoring flock antibodies regularly, evaluating vaccine effectiveness promptly, strengthening pathogen surveillance, and upgrading farm biosecurity level. These measures are essential for safeguarding flock immunity, facilitating early virus detection, and mitigating the risk of HPAI transmission.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".