A risk‐based mutual insurance premium framework for establishing indices of vulnerability to the intentional introduction of transboundary animal diseases
Bibliographic record
Abstract
Biological agents as weapons of agro-crime or agro-terrorism pose threats to peace and economic stability. Such agents pre-exist worldwide as hazards, adversely affecting animal health, as well as imposing substantial burdens on many nations. Few studies have quantified the global risks and vulnerabilities of countries and regions to potential terrorist or criminal operations targeting animal health. We present here a risk-based mutual insurance premium framework for animal health outcomes built upon the World Organisation for Animal Health (WOAH) quantitative risk assessment paradigm. Our objective was to generate dimensionless and relative domain indices related to release and exposure for several biological factors, as well as to assess the preparedness and response ability of each country. We also considered disease-specific measures relating to pathogens, targeted animal populations, the ongoing disease situation, within- and among-country peace or conflict, disease-specific control measures, and the availability of technical tools and personnel for successful disease management. National economic, political, and research and development competencies were used to assess each WOAH Member's potential for resilience. We formulated indices of vulnerability for 25 WOAH Members selected from five worldwide regions; initially, against four transboundary infectious animal diseases that target diverse animal species. We developed these indices using variables obtained from public databases arising from multiple intergovernmental organizations. Subsequently, we compared the relative vulnerability indices among countries for each given disease using three different index building methods: arithmetic mean, distance matrix, and principal component analysis (PCA). The PCA-based approach provided the greatest ability to discriminate among the components and among countries and regions. Due to its transparency and reliance on publicly available datasets, the risk premium framework proposed herein may readily be adjusted by policymakers and agencies and utilized to improve risk management strategies against agro-crime or agro-terror events, as well as for unintentional disease introductions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".