Passive Surveillance for I. scapularis Ticks: Enhanced Analysis for Early Detection of Emerging Lyme Disease Risk
Bibliographic record
Abstract
Lyme disease (LD) is emerging in Canada because of the northward expansion of the geographic range of the tick vector Ixodes scapularis (Say). Early detection of emerging areas of LD risk is critical to public health responses, but the methods to do so on a local scale are lacking. Passive tick surveillance has operated in Canada since 1990 but this method lacks specificity for identifying areas where tick populations are established because of dispersion of ticks from established LD risk areas by migratory birds. Using data from 70 field sites in Quebec visited previously, we developed a logistic regression model for estimating the risk of I. scapularis population establishment based on the number of ticks submitted in passive surveillance and a model-derived environmental suitability index. Sensitivity-specificity plots were used to select an optimal threshold value of the linear predictor from the model as the signal for tick population establishment. This value was used to produce an "Alert Map" identifying areas where the passive surveillance data suggested ticks were establishing in Quebec. Alert Map predictions were validated by field surveillance at 76 sites: the prevalence of established I. scapularis populations was significantly greater in areas predicted as high-risk by the Alert map (29 out of 48) than in areas predicted as moderate-risk (4 out of 30) (P < 0.001). This study suggests that Alert Maps created using this approach can provide a usefully rapid and accurate tool for early identification of emerging areas of LD risk at a geographic scale appropriate for local disease control and prevention activities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".