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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Predicting the spatio-temporal risk of human tick-borne encephalitis (TBE) in Europe by combining hazard and exposure drivers

Authors: Dagostin, Francesca;

Predicting the spatio-temporal risk of human tick-borne encephalitis (TBE) in Europe by combining hazard and exposure drivers

Abstract

This pre-released repository contains all data, code, and model outputs used in the study:"Predicting the spatio-temporal risk of human tick-borne encephalitis (TBE) in Europe by combining hazard and exposure drivers." The materials provided allow for full reproducibility of the analytical workflow described in the manuscript. Due to data-sharing restrictions, the original epidemiological dataset containing human TBE case data cannot be shared publicly. However, a synthetic ("dummy") version of the human case variable is included to ensure that all scripts can be executed and the modeling pipeline reproduced. Contents: 📃 R scripts for model training, simulation, and figure generation 📁 Data/: Covariate datasets and synthetic ("dummy") TBE presence/absence data at NUTS-3 and municipal level 📁 Results/: Fitted model and predicted probabilities of TBE occurrence (2017–2025) at NUTS-3 and municipal levels 📁 Figures/: Figures generated from the results 📁 Folds/: Model folds used for cross-validation All data are provided in .RData format. Detailed README files are included in each folder to guide users through the structure and content. Important Disclaimer: The dummy datasets included in this repository are for illustrative and computational purposes only. They do not reflect the true geographic distribution of TBE and are not intended for analysis or interpretation. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 874850 and is catalogued as MOOD 081. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

Related Organizations
Keywords

Machine Learning, Europe, Tick-Borne Diseases, Disease, Encephalitis, Tick-Borne

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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