Sentinel surveillance of Lyme disease risk in Canada, 2019: Results from the first year of the Canadian Lyme Sentinel Network (CaLSeN)
Bibliographic record
Abstract
Background: Lyme disease is an emerging vector-borne zoonotic disease of increasing public health importance in Canada.As part of its mandate, the Canadian Lyme Disease Research Network (CLyDRN) launched a pan-Canadian sentinel surveillance initiative, the Canadian Lyme Sentinel Network (CaLSeN), in 2019.Objectives: To create a standardized, national sentinel surveillance network providing a real-time portrait of the evolving environmental risk of Lyme disease in each province. Methods:A multicriteria decision analysis (MCDA) approach was used in the selection of sentinel regions.Within each sentinel region, a systematic drag sampling protocol was performed in selected sampling sites.Ticks collected during these active surveillance visits were identified to species, and Ixodes spp.ticks were tested for infection with Borrelia burgdorferi, Borrelia miyamotoi, Anaplasma phagocytophilum, Babesia microti and Powassan virus Results: In 2019, a total of 567 Ixodes spp.ticks (I.scapularis [n=550]; I. pacificus [n=10]; and I. angustus [n=7]) were collected in seven provinces: British Columbia, Manitoba, Ontario, Québec, New Brunswick, Nova Scotia and Prince Edward Island.The highest mean tick densities (nymphs/100 m 2 ) were found in sentinel regions of Lunenburg (0.45), Montréal (0.43) and Granby (0.38).Overall, the Borrelia burgdorferi prevalence in ticks was 25.2% (0%-45.0%).One I. angustus nymph from British Columbia was positive for Babesia microti, a first for the province.The deer tick lineage of Powassan virus was detected in one adult I. scapularis in Nova Scotia.Conclusion: CaLSeN provides the first coordinated national active surveillance initiative for tick-borne disease in Canada.Through multidisciplinary collaborations between experts in each province, the pilot year was successful in establishing a baseline for Lyme disease risk across the country, allowing future trends to be detected and studied.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".