Current and future distribution of Ixodes scapularis ticks in Québec: Field validation of a predictive model
Bibliographic record
Abstract
The incidence of Lyme disease is increasing in Québec and is closely linked to the distribution of Ixodes scapularis ticks. A time-to-establishment model developed in 2012 by Leighton and colleagues predicted the year of tick population establishment for each municipality in eastern Canada. To validate if this model correctly predicted tick distribution in Québec, predicted tick establishment was compared to field data from active tick surveillance (2010-2018) using two criteria: i) the detection of at least one tick and ii) the detection of the three questing stages of the tick. The speed of tick establishment and the increase in the exposed human population by 2100 were predicted with the time-to-establishment model. Field observations were consistent with model predictions. Ticks were detected on average 3 years after the predicted year. The probability of tick detection is significantly higher after the predicted year than before (61% vs 27% of collections). The trend was similar for the detection of three tick stages (16% vs 9% of collections). The average speed of tick range expansion was estimated by the model to be 18 km/year in Québec, with 90% of the human population exposed by 2027. The validation of the time-to-establishment model using field data confirmed that it could be used to project I. scapularis range expansion in Québec, and consequently the increase in Lyme disease risk over the coming decades. This will help public health authorities anticipate and adapt preventive measures, especially in areas not yet affected by Lyme disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".