Predictive Modelling of Tick Distribution: A Machine Learning Approach to <i>Ixodes ricinus</i> Abundance
Bibliographic record
Abstract
ABSTRACT The resurgence of tick‐borne diseases necessitates predictive frameworks that integrate both high accuracy and ecological relevance. This study develops a comprehensive machine learning pipeline to forecast the occurrence of Ixodes ricinus , a principal tick vector in Europe, leveraging high‐dimensional climatic, environmental, and land‐use datasets. We assembled and cleaned regional occurrence datasets from the United Kingdom and wider European repositories, to create a harmonized database comprising over 27,000 verified occurance record. To represent local tick presence and reduce spatial bias, we transformed the point data into 20 km‐wide hexagonal grid cell duplicates. The framework that integrates hexagonal spatial binning, binary transformation, and spatially aware absence selection maintains a balanced 1:2 ratio to minimize sampling bias and spatial autocorrelation. Spatial interpretation was strengthened by adopting DBSCAN with geodesic (haversine) distance, which identifies density‐based clusters and noise points and avoids the Euclidean‐distance constraints inherent to K‐Means. Each observation was paired with dynamic environmental and land‐use variables, including monthly rainfall, NDVI, temperature, and annual land cover. Models were trained and evaluated using stratified fivefold cross‐validation and optimized through RandomizedSearchCV, ensuring efficient exploration of hyperparameter spaces. Comparative evaluation across Random Forest, CatBoost, Gradient Boosting, AdaBoost, and Support Vector Machine classifiers demonstrated high predictive accuracy, with Random Forest achieving an ROC–AUC of 0.941% and F1‐score of 0.882%. Incorporating spatial constraints and temporally aggregated features improved ecological realism and generalisation, addressing prior limitations in temporal dynamics and sampling bias. Feature importance analysis revealed NDVI, rainfall, and temperature as dominant predictors, aligning with ecological expectations. The study centres on tick occurrence, establishing a scalable and robust framework poised to support early warning systems and enable data‐driven surveillance of tick populations across Europe.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".