Companion animal health surveillance systems: An environmental scan
Bibliographic record
Abstract
Monitoring of companion animal zoonotic diseases in Canada is limited by the lack of a comprehensive companion animal health surveillance system, capable of integrating environmental and public health data. To guide the development of a suitable surveillance framework, we conducted an environmental scan of companion animal heath surveillance systems globally. Using academic and grey literature database searches, supplemented with targeted internet searching, we identified 12,718 unique sources. After screening, 257 sources were deemed eligible for inclusion. These sources identified 119 national or regional surveillance and control programs (which were not further characterized) and 33 companion animal health surveillance systems. We extracted information relating to surveillance scope, data source and collection methods, integration of environmental and public health data, and data dissemination methods. In total, 48.5 % (n = 16/33) of the systems relied on submission of data by veterinary professionals or others, whilst 42.4 % (n = 14) extracted data from electronic health records and veterinary diagnostic laboratory data. Surveillance scope included infectious diseases (n = 13), cause of death (n = 2), cancer (n = 1), and toxin exposure (n = 1). Some systems were not focused on specific health outcomes (n = 12). Only 9.1 % (n = 3) of systems integrated environmental or public health data at the point of data collection. However, other systems utilized environmental data during the analysis phase (27.3 %, n = 9). Surveillance systems largely disseminated surveillance outputs through reports (30.3 %, n = 10) and direct feedback to contributors (27.3 %, n = 9). By conducting this environmental scan, we provide a summary of global companion animal health surveillance efforts. Notably, there are few examples of fully integrated companion animal health surveillance systems using a One Health approach.
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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 | 0.000 | 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) | 0.001 | 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".