Améliorer la préparation et la réponse aux crises sanitaires en santé animale : étude de cas du secteur avicole en Indonésie
Bibliographic record
Abstract
The acceleration of globalization, environmental changes, and urbanization in recent decades have created major challenges for animal health and welfare. Events such as the 2009 influenza A (H1N1) and COVID-19 pandemics have highlighted the vulnerability of global food systems to increasingly complex multisectoral health crises with unpredictable consequences. In order to strengthen their response capabilities, many countries and organizations have adopted all-hazards incident management systems (IMS) to support a standardized and systematic response to all types of incidents.However, animal health and welfare management remains poorly integrated into these systems. Veterinary interventions are often organized on an ad hoc basis and disconnected from the overall response, leading to suboptimal management of resource and information. Furthermore, IMS models have been developed mainly by Western countries, and their applicability to other socio-economic contexts remains largely unexplored.This case study-based work focuses on the following research question: What are the practices and needs of stakeholders in the Indonesian poultry sector in terms of animal health management, and to what extent can current incident management systems address these needs?The first part of this work focuses on the study of the poultry sector in Indonesia to illustrate animal health challenges and to describe the impact and potential barriers to the implementation of management policies in a specific context. 41 semi-structured interviews were conducted with 56 public and private stakeholders in the poultry industry in five provinces of Indonesia in July 2022 to explore their priorities, needs, and capacities in terms of animal health management. In the second part of this work, 33 interviews with 45 actors from international organizations were conducted from May to July 2024 to describe the common principles of international IMS and evaluate their practical implementation. Finally, the applicability of these general frameworks to specific local context (such as the poultry sector in Indonesia) is critically discussed and pathways for improvement are suggested.The results revealed that coordination in international responses suffers from a lack of interoperability between information and communication systems, as well as the persistence of disciplinary silos. At the local level, the Indonesian case study showed that official animal health surveillance and management systems had a limited impact and revealed the predominant role of private service providers (veterinarians, pharmaceutical companies, etc.) in disseminating information during crises. The strong compartmentalization between the public and private sectors, the lack of interoperability of production and health data, and the dominance of informal communication channels contribute to the siloed management of health crises. The conventional international response, centered on the detection and eradication of outbreaks, appears ill-suited in a context of endemicity and limited resources, where infectious animal diseases are not perceived as a priority compared to other political, economic, and environmental issues.While IMS help to meet the needs for coordination and information management during crises, their operationalization both at the international and local levels still faces obstacles. There is a need to re-thing the scale of application of these systems, involve all stakeholders in the decision-making process, and strengthen organizational memory to ensure that lessons learned translate into meaningful changes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".