Machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe
Most Cited Paper
in Vector-borne infectious diseases
18 Months Post-Publication
Class of 2026
“Machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe”
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Topics
tick-borne pathogens · machine learning algorithms · Ixodes ricinus · Dermacentor reticulatus · Dermacentor marginatus · Hyalomma marginatum · species distribution modeling · Random forest · Gradient Boosting · Neural Networks · Naive Bayes · AdaBoost · species distribution modeling · land cover variables · land use variables · vertebrate host distribution · habitat fragmentation · trait-matching · early warning systems · tick seasonality
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Pepkio Research Index (PRI). Topics and Trends in Most Cited Vector-borne infectious diseases Papers, Class of 2026. https://pri.pepkio.com/top-papers/vector-borne-infectious-diseases/2026. Accessed 2026-07-31. Methodology Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459