A Canadian Application of One Health: Integration of <i>Salmonella</i> Data from Various Canadian Surveillance Programs (2005–2010)
Bibliographic record
Abstract
Most bacterial pathogens associated with human enteric illness have zoonotic origins and can be transmitted directly from animals to people or indirectly through food and water. This multitude of potential exposure routes and sources makes the epidemiology of these infectious agents complex. To better understand these illnesses and identify solutions to reduce human disease, an integrative approach like One Health is needed. This article considers the issue of Salmonella in Canada and interprets data collected by several Canadian surveillance and research programs. We describe recovery of Salmonella from various samples collected along the exposure pathway and compare the serovars detected in the different components under surveillance (animal, food, environment, and human). We then present three examples to illustrate how an approach that interprets multiple sources of surveillance data together is able to address issues that transcend multiple departments and jurisdictions. First, differences observed in recovery of Salmonella from different cuts of fresh chicken collected by different programs emphasize the importance of considering the surveillance objectives and how they may influence the information that is generated. Second, the high number of Salmonella Enteritidis cases in Canada is used to illustrate the importance of ongoing, concurrent surveillance of human cases and exposure sources to information domestic control and prevention strategies. Finally, changing patterns in the occurrence of ceftiofur-resistant Salmonella Heidelberg in retail meats and humans demonstrates how integrated surveillance can identify an issue in an exposure source and link it to a trend in human disease. Taken together, surveillance models that encompass different scales can leverage infrastructure, costs, and benefits and generate a multidimensional picture that can better inform disease prevention and control programs.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".