The International Response to Highly Pathogenic Avian Influenza: Science, Policy and Politics
Bibliographic record
Abstract
Over the last decade, the avian influenza virus, H5N1, has spread across most of Asia and Europe and parts of Africa. In some countries – including Indonesia, China, Vietnam, Bangladesh, Nigeria and Egypt – the avian disease has probably become endemic. There has, as yet, been no human pandemic, although 245 deaths have been reported since 2003. A major international response has been launched, backed by over $2 billion of public money. Huge numbers of poultry have been culled, vaccination campaigns have been implemented and markets have been restructured. These efforts have affected the livelihoods and businesses of millions. In addition, substantial efforts have been invested in improving human and animal health systems, combined with major investments in drug and vaccine development. Detailed contingency and preparedness plans have been devised in case a pandemic occurs. This paper asks: what lessons can we learn from this experience, and what does this mean for future efforts to respond to emerging infectious diseases under the One World, One Health initiative? The paper explores three core narratives that have shaped the response: one focuses on veterinary issues, another on human public health and a third on pandemic preparedness. All have common characteristics, emphasising outbreak control and containment. Missing dimensions are identified, including a lack of attention to underlying disease drivers, issues of poverty and equity and broader questions of access and governance. The paper examines how discourses of security and risk pervade the discussions, affecting how the response has played out. The paper concludes with a discussion of the emerging challenges, including the implications for organisational architectures, professional training and programme implementation.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".