Modeling spatio-temporal annual changes in the probability of human tick-borne encephalitis (TBE) occurrence across Europe
Authors: Dagostin Francesca , Erazo Diana , Marini Giovanni , Da Re Daniele , Tagliapietra Valentina , Avdicova Maria , Avšič –Županc Tatjana , Dub Timothée , Fiorito Nahuel , Knap Nataša , Gossner Céline M. , Kerlik Jana , Mäkelä Henna , Markowicz Mateusz , Olyazadeh Roya , Richter Lukas , Wint William , Zuccali Maria Grazia , Žygutienė Milda , Dellicour Simon , Rizzoli Annapaola 
Published: October 2024

Introduction

Tick-borne encephalitis (TBE), caused by tick-borne encephalitis virus (TBEV), is a zoonotic disease that can cause severe neurological symptoms. Given the increasing number of reported human TBE cases in Europe, a spatio-temporal predictive model to infer the year-to-year probability of human TBE occurrence across Europe at the regional and municipal administrative levels was developed.

Methods

The distribution of human TBE cases at the regional (NUTS-3) level during the period 2017-2022, was derived by using data provided by the European surveillance system (TESSy, ECDC), and at the municipal level by using data from Austria, Finland, Italy, Lithuania, and Slovakia. The probability of presence of human TBE cases at the regional and municipal levels for the period 2017-2024 was modeled with a boosted regression trees model, including covariates that affect both the natural hazard of virus circulation and human exposure to tick bites.

Results

Areas with the highest probability of human TBE infections are primarily located in central-eastern Europe, the Baltic states, and along the coastline of Nordic countries up to the Bothnian Bay. Our results also highlight a statistically significant rising trend in the probability of human TBE infections not only in north-western, but also in south-western European countries, offering a spatio-temporal predictive framework for the assessment of areas where human TBE infection are most likely to occur. The model showed good predictive performance, with a mean AUC of 0.85 (SD = 0.02), sensitivity of 0.82, and specificity of 0.80 at the regional level, and a mean AUC of 0.82 (SD = 0.03), sensitivity of 0.80, and specificity of 0.69 at the municipal level.

Discussion

With ongoing climate and land use changes, the number of human TBE cases is likely to increase and expand into new areas, as trends are already indicating. This underscores the need for predictive models that can help prioritize intervention efforts. The approach adopted, by leveraging lagged covaries, enables timely one-year-ahead predictions, thus supporting surveillance, prevention, and control of human TBE infections by public health authorities.

Statements

Ethical statement

Ethical approval was not needed.

Funding statement

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.

Conflict of interest

None.

Authors’ contributions

Francesca Dagostin: Conceptualization, Methodology, Data Curation, Formal Analysis, Writing - Original Draft. Diana Erazo: Conceptualization, Methodology, Writing - Review & Editing. Giovanni Marini: Conceptualization, Methodology, Writing - Review & Editing. Daniele Da Re: Conceptualization, Methodology, Writing - Review & Editing. Valentina Tagliapietra: Conceptualization, Methodology, Writing - Review & Editing. Maria Avdicova: Resources, Writing - Review & Editing. Tatjana Avšič – Županc: Resources, Writing - Review & Editing. Timothée Dub: Conceptualization, Resources, Writing - Review & Editing. Nahuel Fiorito: Resources, Writing - Review & Editing. Nataša Knap: Resources, Writing - Review & Editing. Céline M. Gossner: Resources, Writing - Review & Editing. Jana Kerlik: Resources, Writing - Review & Editing. Henna Mäkelä: Resources, Writing - Review & Editing. Mateusz Markowicz: Resources,Writing - Review & Editing. Roya Olyazadeh: Resources, Writing - Review & Editing. Lukas Richter: Resources, Writing - Review & Editing. William Wint: Resources, Writing - Review & Editing. Maria Grazia Zuccali: Resources, Writing - Review & Editing. Milda Žygutiene: Resources, Writing - Review & Editing. Simon Dellicour: Methodology, Writing - Review & Editing. Annapaola Rizzoli: Conceptualization, Methodology, Writing - Review & Editing.

Data availability

The data that support the findings of this study were provided by ECDC, Azienda Provinciale per i Servizi Sanitari Provincia Autonoma di Trento (APSS), Unità Locale Socio Sanitaria Dolomiti (ULSS N.1 Dolomiti), Public Health Authority of the Slovak Republic, Austrian Agency for Health and Food Safety (AGES), Finnish Institute for Health and Welfare (THL), National Public Health Center under the Ministry of Health (Lithuania) and University of Ljubljana. Restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. The interactive risk maps can be explored in detail athttps://mood-platform.avia-gis.com.

Disclaimer

The views and opinions of the authors expressed herein do not necessarily state or reflect those of ECDC. The accuracy of the authors’ statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data and for data management, data merging and data collation after provision of the data. ECDC shall not be held liable for improper or incorrect use of the data.