Forecasting Tick-Borne Encephalitis Based on Climatic Factors Using a Negative Binomial Model in the Baltic State Countries
1
Department of environmental and occupational medicine, Lithuanian University of Health Sciences, Kaunas, Lithuania
2
Health Research Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
3
Department of Physics, Mathematics and Biophysics, Lithuanian University of Health Sciences, Kaunas, Lithuania
4
Vilnius Public Health Bureau, Vilnius, Lithuania
5
Strategic Planning Services, International Project Centre of Excellen, Tallinn Strategic Management Office, Tallinn, Estonia
6
City Development Department, Riga City Council, Riga, Latvia
7
Kaunas Public Health Bureau, Kaunas, Lithuania
Popul. Med. 2026;8(Supplement Supplement 1):
ABSTRACT
BACKGROUND:
Tick-borne encephalitis (TBE) continues to be a major health concern across the Baltic region, with increasing incidence reported in Lithuania, Latvia, and Estonia. Climatic changes—particularly warmer winters and prolonged vegetation periods—have enhanced tick survival and seasonal activity [1]. Recent studies in Estonia highlight the underestimated risk of TBE in urban green areas, where infected ticks have been detected[2]. Similar findings in Lithuania and Latvia confirm that climatic variability significantly influences TBE virus prevalence, though socio-ecological and behavioural factors remain critical determinants of exposure [3,4]. The study is funded by the European Union (AURORA under Grant Agreement) [5].
METHODS:
The dataset comprises quantitative monthly count data on reported TBE cases together with corresponding meteorological indicators, including mean monthly temperature, humidity and atmospheric pressure. Data from Vilnius (2010-2024), Riga (2006-2023), and Tallinn (1993-2019) were used in the analysis. Negative binomial regression was applied because it includes an additional dispersion parameter, which allows the model to account for overdispersion and more accurately reflect the variability present in the data.
RESULTS:
The negative binomial regression model for Vilnius data showed that temperature (°C), relative humidity (%), and atmospheric pressure (hPa) had a significant (p<0.01) effect on the monthly morbidity of TBE, and independent climate variables' coefficients (B) are respectively 0.206, 0.072, and 0.044. Riga’s monthly number of TBE cases showed a significant positive relationship with temperature (B = 0.217, p<0.001) and humidity (B = 0.053, p<0.001). The Tallinn model revealed similar
RESULTS:
the temperature and relative humidity coefficients show a significant positive relationship with the monthly number of TBE cases, with respective B coefficients of 0.208 and 0.063 (p<0.001).
CONCLUSIONS:
The findings indicate that rising temperatures and humidity intensify TBE spread, highlighting the need to prioritise climate-driven vector surveillance and control measures and implement a warning system to manage increases in case numbers.
Tick-borne encephalitis (TBE) continues to be a major health concern across the Baltic region, with increasing incidence reported in Lithuania, Latvia, and Estonia. Climatic changes—particularly warmer winters and prolonged vegetation periods—have enhanced tick survival and seasonal activity [1]. Recent studies in Estonia highlight the underestimated risk of TBE in urban green areas, where infected ticks have been detected[2]. Similar findings in Lithuania and Latvia confirm that climatic variability significantly influences TBE virus prevalence, though socio-ecological and behavioural factors remain critical determinants of exposure [3,4]. The study is funded by the European Union (AURORA under Grant Agreement) [5].
METHODS:
The dataset comprises quantitative monthly count data on reported TBE cases together with corresponding meteorological indicators, including mean monthly temperature, humidity and atmospheric pressure. Data from Vilnius (2010-2024), Riga (2006-2023), and Tallinn (1993-2019) were used in the analysis. Negative binomial regression was applied because it includes an additional dispersion parameter, which allows the model to account for overdispersion and more accurately reflect the variability present in the data.
RESULTS:
The negative binomial regression model for Vilnius data showed that temperature (°C), relative humidity (%), and atmospheric pressure (hPa) had a significant (p<0.01) effect on the monthly morbidity of TBE, and independent climate variables' coefficients (B) are respectively 0.206, 0.072, and 0.044. Riga’s monthly number of TBE cases showed a significant positive relationship with temperature (B = 0.217, p<0.001) and humidity (B = 0.053, p<0.001). The Tallinn model revealed similar
RESULTS:
the temperature and relative humidity coefficients show a significant positive relationship with the monthly number of TBE cases, with respective B coefficients of 0.208 and 0.063 (p<0.001).
CONCLUSIONS:
The findings indicate that rising temperatures and humidity intensify TBE spread, highlighting the need to prioritise climate-driven vector surveillance and control measures and implement a warning system to manage increases in case numbers.
| eISSN: | 2654-1459 |
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