Predicting the spatio-temporal risk of human tick-borne encephalitis (TBE) in Europe by combining hazard and exposure drivers
Contributors
Contact person:
Researcher (20):
- Erazo, Diana2
- Marini, Giovanni1
- Da Re, Daniele
- Tagliapietra, Valentina1
- Avdicova, Maria3
- Avšič – Županc5, Tatjana4
- Dub, Timothée5
- Fiorito, Nahuel6
- Knap, Nataša4
- Gossner, Céline M.7
- Kerlik, Jana3
- Mäkelä, Henna5
- Markowicz, Mateusz8
- Olyazadeh, Roya9
- Richter, Lukas8
- Wint, William9
- Zuccali, Maria Grazia10
- Žygutienė, Milda11
- Dellicour, Simon2
- Rizzoli, Annapaola1
- 1. Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige (TN), Italy
- 2. Spatial Epidemiology Lab, Université Libre de Bruxelles, Bruxelles, Belgium
- 3. Regional Authority of Public Health in Banská Bystrica, Banská Bystrica, Slovakia
- 4. Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- 5. Department of Health Security, Finnish Institute for Health and Welfare, Helsinki, Finland
- 6. Unità Locale Socio Sanitaria Dolomiti, Belluno, Italy
- 7. European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
- 8. Austrian Agency for Health and Food Safety, Vienna, Austria
- 9. Environmental Research Group Oxford Ltd, c/o Dept Biology, Oxford, United Kingdom
- 10. Azienda Provinciale Servizi Sanitari, Dipartimento di prevenzione, Trento, Italy
- 11. National Public Health Center under the Ministry of Health, Vilnius, Lithuania
Description
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.
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Additional details
Funding
Dates
- Available
-
2025-06-25
Software
- Programming language
- R