000145443 001__ 145443
000145443 005__ 20241030091919.0
000145443 0247_ $$2doi$$a10.1080/07853890.2024.2405074
000145443 0248_ $$2sideral$$a140310
000145443 037__ $$aART-2024-140310
000145443 041__ $$aeng
000145443 100__ $$0(orcid)0000-0001-7483-046X$$aEstrada-Peña, Agustín$$uUniversidad de Zaragoza
000145443 245__ $$aMachine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe
000145443 260__ $$c2024
000145443 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145443 5203_ $$aBackground
Tick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their impact on human health requires models covering large geographic areas and considering both the abiotic traits that affect tick presence, as well as the vertebrates used as hosts, vegetation, and land use. Herein, we integrated the public information available for Europe regarding the variables that may affect habitat suitability for ticks and hosts and tested five machine learning algorithms (MLA) for predicting the distribution of four prominent tick species across Europe.
Materials and methods
A grid of cells 20 km in diameter was prepared to cover the entire territory, containing data on vegetation, points of water, habitat fragmentation, forest density, grass extension, or imperviousness, with information on temperature and water deficit. The distribution of the hosts (162 species) was modelled and included in the dataset. We used five MLA, namely, Random Forest, Neural Networks, Naive Bayes, Gradient Boosting, and AdaBoost, trained with reliable coordinates for Ixodes ricinus, Dermacentor reticulatus, Dermacentor marginatus, and Hyalomma marginatum in Europe.
Results
Both Random Forest and Gradient Boosting best predicted ticks and host environmental niches. Our results demonstrate that MLA can identify trait-matching combinations of environmental niches. The inclusion of land cover and land use variables has a superior capacity for predicting areas suitable for ticks, compared to classic methods based on the use of climate data alone.
Conclusions
Flexible MLA-driven models may offer several advantages over traditional models. We anticipate that these results may be extrapolated to other regions and combinations of tick-vertebrates. These results highlight the potential of MLA for inference in ecology and provide a background for the evolution of a completely automatized tool to calculate the seasonality of ticks for early warning systems aimed at preventing tick-borne diseases.
000145443 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145443 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145443 700__ $$ade la Fuente, José
000145443 7102_ $$11009$$2773$$aUniversidad de Zaragoza$$bDpto. Patología Animal$$cÁrea Sanidad Animal
000145443 773__ $$g56, 1 (2024), 2405074 [13 pp.]$$pAnn. med.$$tANNALS OF MEDICINE$$x0785-3890
000145443 8564_ $$s1616610$$uhttps://zaguan.unizar.es/record/145443/files/texto_completo.pdf$$yVersión publicada
000145443 8564_ $$s936149$$uhttps://zaguan.unizar.es/record/145443/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145443 909CO $$ooai:zaguan.unizar.es:145443$$particulos$$pdriver
000145443 951__ $$a2024-10-30-08:48:35
000145443 980__ $$aARTICLE