Prioritizing Zoonotic Diseases: Differences in Perspectives Between Human and Animal Health Professionals in North America
Bibliographic record
Abstract
Zoonoses pose a significant burden of illness in North America. Zoonoses represent an additional threat to public health because the natural reservoirs are often animals, particularly wildlife, thus eluding control efforts such as quarantine, vaccination and social distancing. As there are limited resources available, it is necessary to prioritize diseases in order to allocate resources to those posing the greatest public health threat. Many studies have attempted to prioritize zoonoses, but challenges exist. This study uses a quantitative approach, conjoint analysis (CA), to overcome some limitations of traditional disease prioritization exercises. We used CA to conduct a zoonoses prioritization study involving a range of human and animal health professionals across North America; these included epidemiologists, public health practitioners, research scientists, physicians, veterinarians, laboratory technicians and nurses. A total of 699 human health professionals (HHP) and 585 animal health professionals (AHP) participated in this study. We used CA to prioritize 62 zoonotic diseases using 21 criteria. Our findings suggest CA can be used to produce reasonable criteria scores for disease prioritization. The fitted models were satisfactory for both groups with a slightly better fit for AHP compared to HHP (84.4% certainty fit versus 83.6%). Human-related criteria were more influential for HHP in their decision to prioritize zoonoses, while animal-related criteria were more influential for AHP resulting in different disease priority lists. While the differences were not statistically significant, a difference of one or two ranks could be considered important for some individuals. A potential solution to address the varying opinions is discussed. The scientific framework for disease prioritization presented can be revised on a regular basis by updating disease criteria to reflect diseases as they evolve over time; such a framework is of value allowing diseases of highest impact to be identified routinely for resource allocation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".