SARS-CoV-2 Surveillance in Peri-Domestic and Wildlife Species in Ontario, Canada
Bibliographic record
Abstract
Methods & Materials: Documentation on the outbreak response from the Ministries of Environment.Water and Agriculture (MEWA) and Health (MOH) were reviewed.With an experienced wildlife veterinarian from the Wildlife Authority added, the review team sought to understand the possible transmission pathways to the sand cat.Additonally, it sought to characterize the potential direct and indirect public health threats to man and animalsResults: The rabies outbreak and spillover event were due to an unprovoked attack on a 55-year-old farmer on his farm by a sand cat in Sharri, a village in the Qaseem region of Saudi Arabia.The victim killed the cat prior to presenting for medical attention.The coordinated and rapid One Health response between MEWA and MOH led to the rapid diagnosis of rabies in the brain of the sand cat using Direct Fluorescent Antibody testing.The small size and solitary life style of the sand cat in its harsh, desert habitat in Central Saudi Arabia provoked theories of cross-species transmission from known primary reservoirs: bats or foxes.The prevailing passive nature of rabies surveillance and the lack of a robust laboratory surveillance mechanism precluded confirmation of these theories by viral characterization.We observed that ongoing uncontrolled illegal poaching and online sales of sand cat could serve as pathways for future rabies spillovers in man and other animals.Additionally, spillovers can potentially involve other pathogens primarily or secondarily harbored by sand cats.Conclusion: This first report of rabies in sand cats further highlights how the One Health approach in disease control and prevention and pathogen spillover monitoring.Furthermore, enhanced rabies spillover surveillance at the wildlife-domestic animal interface can be leveraged with to understand other possible pathogen spillovers in our desert ecosystem.The need for strengthening the domestic and wildlife surveillance mechanism, including having dedicate animal control officers, cannot be overemphasized.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".