Spatial patterns of <i>Borrelia burgdorferi</i>, <i>Borrelia miyamotoi</i> and <i>Anaplasma phagocytophilum</i> detected in <i>Ixodes</i> spp. ticks from Canadian companion animals, 2019–2020
Bibliographic record
Abstract
Increasing temperatures due to climate change have contributed to a northward range expansion of Ixodes scapularis ticks in Canada. These ticks harbour pathogens of public and animal health significance, including Borrelia burgdorferi and Anaplasma phagocytophilum, which cause Lyme disease and anaplasmosis, respectively, in humans, dogs and horses, and Borrelia miyamotoi, which causes a flu-like relapsing fever in humans. To address the risks associated with these vector-borne zoonotic diseases, continuous tick surveillance is advised. This study examined spatial patterns of B. burgdorferi, B. miyamotoi and A. phagocytophilum from ticks submitted through a national study on ticks of companion animals. From 1 April 2019 to 31 March 2020, we received a total of 1541 eligible submissions from 94 veterinary clinics across Canada. Individual and pooled samples of a maximum of either 5 I. scapularis, I. pacificus or I. angustus samples from the same animal and of the same life stage were screened using real-time PCR targeting genes 23S rRNA for Borrelia spp. and msp2 for A. phagocytophilum. Confirmatory testing was conducted on all 23S rRNA positive samples using a duplex assay for ospA and flaB to differentiate B. burgdorferi and B. miyamotoi, respectively. Prevalence estimates were highest (>20%) for B. burgdorferi in southwestern Manitoba, eastern Ontario, southwestern Quebec, New Brunswick and Nova Scotia. Estimates of B. miyamotoi and A. phagocytophilum were much lower (<5%), except for higher A. phagocytophilum (>5%) estimates for southern Manitoba, eastern Ontario and Prince Edward Island. Findings from this study, combined with other surveillance approaches, can be used to guide veterinary and public health approaches for ticks and tick-borne diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".