Earth Observation-Informed Risk Maps of the Lyme Disease Vector Ixodes scapularis in Central and Eastern Canada
Bibliographic record
Abstract
Climate change is facilitating the geographic range expansion of populations of the tick vector of Lyme disease Ixodes scapularis in Canada. Here, we characterize and map the spatio-temporal variability of environments suitable for I. scapularis using Earth observation (EO) data. A simple algorithm for I. scapularis occurrence (cumulative degree-days and forest: CSDF) was developed by combining cumulative annual surface degree-days above 0 °C and forest cover. To map the environmental risk of I. scapularis (risk of I. scapularis: RIS) in central and eastern Canada from 2000 to 2015, CSDF was adjusted using data from an I. scapularis population model. CSDF was validated using cumulative annual degree days >0 °C (CADD) from meteorological stations, and CSDF was strongly associated with CADD (n = 52, R2 > 0.86, p < 0.001). Data on field surveillance for I. scapularis ticks (2008 to 2018) were used to validate the risk maps. The presence of I. scapularis ticks was significantly associated with CSDF, and at a limit of 2800, sensitivity approached 100%. RIS increased over the study period, with the highest values in 2012 and the lowest in 2000. The RIS was on average higher in Ontario and Quebec compared to other provinces, and it was higher in the southern parts of the provinces. The proportion of the populated areas with a positive RIS increased on average in central and eastern Canada from 2000 to 2015. Predicted I. scapularis occurrence identifies areas with a more probable risk of tick bites, Lyme disease, and other I. scapularis-borne diseases, which can help guide targeted surveillance, prevention, and control interventions.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".