Quality over quantity in active tick surveillance: Sentinel surveillance outperforms risk-based surveillance for tracking tick-borne disease emergence in southern Canada
Bibliographic record
Abstract
Background: Lyme disease (LD) emerged in southern Québec at the start of the century, with many municipalities now endemic. A coordinated active surveillance programme has been in place in the province of Québec since 2014, including a limited number of sentinel field sites resampled each year and a larger set of accessory field sites that change yearly according to the LD surveillance signal. We aimed to evaluate whether a sentinel approach to active surveillance was more representative of LD risk to human populations, compared to risk-based surveillance. Methods: We compared enzootic hazard measures (average nymph densities) from sentinel and accessory sites with LD risk (number of human LD cases) across the study area between 2015 and 2019 using local bivariate Moran's I analysis. Results: <0.001). In addition, sentinel sites successfully tracked the interannual trend in LD case numbers, whereas accessory sites showed no association despite the larger sample size. Conclusion: Where surveillance aims to document changes in tick-borne disease risk over time and space, we suggest that repeated sampling of carefully selected field sites may be most effective, while risk-based surveillance may be more usefully applied to confirm the presence of emerging disease risk in a specific region of interest or to identify suitable sites for long-term monitoring as LD and other tick-borne diseases continue to emerge.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".