Dogs (Canis familiaris) as Sentinels for Human Infectious Disease and Application to Canadian Populations: A Systematic Review
Bibliographic record
Abstract
In a world where climate change, vector expansion, human activity, and pathogen dispersal do not respect boundaries, the human–animal–pathogen interface has become less defined. Consequently, a One Health approach to disease surveillance and control has generated much interest across several disciplines. This systematic review evaluates current global research on the use of domestic dogs as sentinels for human infectious disease, and critically appraises how this may be applied within Canada. Results highlighted a bias in research from high- and middle-income-economy countries, with 35% of the studies describing data from the Latin America/Caribbean region, 25% from North America, and 11% from the European/Central Asia region. Bacteria were the most studied type of infectious agent, followed by protozoa, viruses, helminths, and fungi. Only six out of 142 studies described disease in Canada: four researched a variety of pathogens within Indigenous communities, one researched Borrelia burgdorferi in British Columbia, and one researched arboviruses in Quebec. Results from this review suggest that dogs could provide excellent sentinels for certain infectious-disease pathogens in Canada, yet are currently overlooked. Further research into the use of dog-sentinel surveillance is specifically recommended for California serogroup viruses, Chikungunya virus, West Nile virus, Lyme borreliosis, Rickettsia spp., Ehrlichia spp., and Dirofilaria immitis.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".