Surveillance for Ixodes scapularis and Ixodes pacificus ticks and their associated pathogens in Canada, 2020
Bibliographic record
Abstract
Background: Ixodes scapularis and Ixodes pacificus ticks are the principal vectors of the agent of Lyme disease and several other tick-borne diseases in Canada.Tick surveillance data can be used to identify local tick-borne disease risk areas and direct public health interventions.The objective of this article is to describe the seasonal and spatial characteristics of the main Lyme disease vectors in Canada, and the tick-borne pathogens they carry, using passive and active surveillance data from 2020.Methods: Passive and active surveillance data were compiled from the National Microbiology Laboratory Branch (Public Health Agency of Canada), provincial and local public health authorities, and eTick (an online, image-based platform).Seasonal and spatial analyses of ticks and their associated pathogens are presented, including infection prevalence estimates.Results: In passive surveillance, I. scapularis (n=7,534) were submitted from all provinces except Manitoba and British Columbia, while I. pacificus (n=718) were submitted only from British Columbia.No ticks were submitted from the Territories.The seasonal distribution of I. scapularis submissions was bimodal, but unimodal for I. pacificus.Four tick-borne pathogens were identified in I. scapularis (Borrelia burgdorferi, Anaplasma phagocytophilum, Babesia microti and Borrelia miyamotoi) and one in I. pacificus (B.miyamotoi).In active surveillance, I. scapularis (n=688) were collected in Ontario, Québec and New Brunswick.Five tick-borne pathogens were identified: B. burgdorferi, A. phagocytophilum, B. microti, B. miyamotoi and Powassan virus. Conclusion:This article provides a snapshot of the distribution of I. scapularis and I. pacificus and their associated human pathogens in Canada in 2020, which can help assess the risk of exposure to tick-borne pathogens in different provinces.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".