A Cost–Benefit Analysis of Preparing National Veterinary Services for Transboundary Animal Disease Emergencies
Bibliographic record
Abstract
The natural, accidental, or deliberate release of pathogens into livestock populations carries with it a range of consequences for society, from zoonotic disease outbreaks, to changes in food security and economic welfare. An important contribution to mitigating the risk of disease outbreaks comes from having well-prepared emergency response plans and agencies with the capacity to put those plans into operation. In the case of animal disease, national Veterinary Services (VS) take a central role. Unknown and uncertain events, such as if, when and where the next disease outbreak will occur makes economic decision-making a challenge. While the costs of preparing for emergencies can be quantified in a conventional manner, the scope, scale, and likelihood of benefits actually accruing are all subject to uncertainty. This study attempts to examine the costs and benefits of preparing national VS for animal disease emergencies, including natural, accidental, or deliberate release of pathogens. Data collected as part of the World Organisation for Animal Health's Performance of VS program for countries in East and West Africa and South East Asia were used for estimating investment costs. A state-contingent approach is used to constrain the uncertainty space in terms of disease impact. The probability of a disease event occurring and the probability of that event being contained by emergency preparation are used to describe a frontier at which investment breaks-even in a variety of scenarios. An increased probability of breaking-even on investment was found with high livestock numbers per capita and increasing intensification in livestock production systems. The method and findings provide a means to understand the benefits of preparing for uncertain events and are aimed to further the dialogue around policy development for livestock disease emergencies in lower-income countries.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".