A call for a coherent One Health strategy for the surveillance of climate-sensitive infectious diseases in the Canadian Arctic and subarctic regions
Bibliographic record
Abstract
INTRODUCTION: The increased burden of climate-sensitive infectious diseases (CSIDs) within the circumpolar region, one of the many impacts of climate change, is impacting human, animal and ecosystem health. An integrated One Health approach to surveillance of CSIDs has been promoted by the scientific community as a prerequisite to enhance preparedness and response. Up to now, little is known about how the One Health approach has been implemented in surveillance systems for CSIDs in the Arctic and surrounding regions. OBJECTIVES: The objectives of this study were to map surveillance activities currently implemented in the Canadian Arctic and subarctic for the 16 CSID identified by the Arctic Council, to describe how One Health has been operationalized in these activities, and to explore the integration and leadership of Indigenous partners in current surveillance systems. METHOD: We performed the mapping in three steps: a rapid review of the scientific literature, a review of the grey literature and an online questionnaire sent to key stakeholders involved in CSID surveillance in the Canadian Arctic and subarctic regions. RESULTS AND CONCLUSIONS: We identified 37 scientific peer-reviewed and 58 grey literature records. We mapped (1) surveillance of mandatory notifiable diseases at the federal, provincial or territorial levels not specific to the Arctic and subarctic regions, and (2) non-mandatory surveillance programs specific to the Arctic and subarctic regions. We described programs targeting either a single disease, human populations or wildlife. In most programs, there was no explicit mention of the integration of the One Health approach, and little information was available on collaboration efforts between sectors. Programs involved Indigenous communities at various levels, ranging from very low communication to community members, to high involvement and leadership in program management. Improvement in current CSID surveillance activities in Canada should include enhancing information accessibility, ensuring geographic representation, fostering sustainability in implementation of One Health strategies, and stronger involvement of Indigenous communities in the leadership of surveillance systems. An internationally harmonised approach across the Arctic and subarctic regions for all CSIDs has the potential to unify circumpolar surveillance efforts, save resources, and ultimately better inform public health authorities on the actions to prioritize in the context of climate change.
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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 | 0.080 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".