An Evaluation of Spatial Lyme Disease Risk at Regional and Health Unit Scales Using Remotely Sensed Surface Temperature, GIS-Based Habitat Suitability Data and Population Modelling
Bibliographic record
Abstract
The spread of Lyme disease continues to be a severe public health concern in Ontario, Canada due to rising temperatures. Several previous studies have modelled the extent and rate of risk increase at provincial and national scales using climate-based population modelling of the black-legged tick. The purpose of this study is to evaluate the applicability of this approach at a regional and health unit scale with high-resolution remotely sensed (RS) temperature data and GIS-based habitat suitability data. These data are input into a tick population model to calculate the basic reproductive number (R0), an indicator of reproductive success in a given environment. Monthly average RS land surface temperature data from 2008 to 2017 are used as model inputs to evaluate R0 values in eastern Ontario, and 8-day average RS land surface temperature data from 2016 to 2017 are used to evaluate R0 values in the Kingston, Frontenac, and Lennox & Addington health unit region. It is found that there was an overall increase in R0 over eastern Ontario, up to a maximum rate of change of 0.28 ticks which survive to reproductive age per tick per year. The rate of change of R0 is not significantly impacted by elevation based upon local regression analysis, but is impacted by land cover type. Model outputs are validated using Lyme disease exposure information collected by Public Health Ontario. At the health unit scale, tick host density is varied according to habitat suitability to evaluate its impact relative to temperature. When host density is accounted for, urban areas become less suitable and forested areas become more suitable. This study provides increased insight into Lyme disease risk modelling at the regional and health unit scales, and the impact of tick host dynamics on habitat suitability at the health unit scale.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".