The Economic Impact of High Consequence Zoonotic Pathogens: Why Preparing for these is a Wicked Problem
Bibliographic record
Abstract
Abstract: This paper reviews literature on the economic impacts of outbreaks and control strategies for high consequence zoonotic priority diseases, ie. zoonotic diseases that are generally FADs, zoonotic diseases that occur rarely, or zoonotic diseases that have bioterrorist potential sufficient to be important for the United States. Such diseases are referred to here as zoonotic priority diseases (ZPDs). These ZPDs are categorized into three levels of economic impact: high, moderate, and low with the recognition that there are aspects of each of these diseases that could make the categorization presented here inaccurate. Arguments are made for why determination of optimal ZPD and more generally FAD preparedness and response strategies are wicked problems. The paper concludes with the implications for further development of appropriate ZPD policy and some needs for further analyses.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".