Forecasting Lyme Borreliosis Based on Climatic Factors Using a Negative Binomial Model in the Baltic States
1
Department of Environmental and Occupational Medicine, Lithuanian University Of Health Sciences, Kaunas, Lithuania
2
Department of Physics, Mathematics and Biophysics,, Lithuanian University of Health Sciences, Kaunas, Lithuania
3
Vilnius Public Health Bureau, Vilnius, Lithuania
4
Strategic Planning Services, International Project Centre of Excellence, Tallinn Strategic Management Office, Tallinn, Estonia
5
City Development Department, Riga City Council, Riga, Latvia
6
Kaunas Public Health Bureau, Kaunas, Lithuania
Popul. Med. 2026;8(Supplement Supplement 1):A302
ABSTRACT
BACKGROUND:
Lyme borreliosis (LB) is the most common tick-borne disease in Europe, with an increasing incidence over time [1]. The incidence is increasing due to climate change and has been linked to warm winters, higher summer temperatures, lower seasonal temperature variation, and higher vegetation indices. The highest incidences were reported in Baltic states, Nordic countries and Western Europe [2,3]. This work aims to provide a prognostic model for LB in the Baltic states as part of the forecasting and early warning system of the AURORA project [4].
METHODS:
Surveillance data, reporting the number of cases per month of LB in Vilnius (Lithuania), Riga (Latvia), and Tallinn (Estonia) and meteorological data were used to conduct the epidemiological modelling. The availability of historical data varied across countries: the analysis included data from 2010-2024 for Vilnius, 2006-2023 for Riga, and 1993-2019 for Tallinn. Negative binomial regression was used to predict the monthly LB incidence.
RESULTS:
The model for Vilnius data showed that temperature (°C), relative humidity (%), precipitation (mm), and atmospheric pressure (hPa) were significantly associated with monthly LB morbidity (p<0.05), with regression coefficients (B) of 0.137, 0.074, -0.148, and 0.04, respectively. In Riga, the monthly number of LB cases showed a significant positive relationship with temperature (B=0.144, p=0.001) and humidity (B=0.061, p=0.001). However, average wind speed (m/s) had a negative effect, with a one-unit increase corresponding to a 57.3% reduction in cases. The Tallinn model revealed similar
RESULTS:
the temperature and relative humidity coefficients showed a significant positive relationship with the monthly LB cases, with coefficients of 0.111 and 0.078, respectively; average wind speed had a negative effect (B=-0.785, p=0.001).
CONCLUSIONS:
Higher temperatures and increased humidity increase the prevalence of the LB across the Baltic states, while wind reduces transmission, highlighting the need for climate-based monitoring, control, and early warning systems.
Lyme borreliosis (LB) is the most common tick-borne disease in Europe, with an increasing incidence over time [1]. The incidence is increasing due to climate change and has been linked to warm winters, higher summer temperatures, lower seasonal temperature variation, and higher vegetation indices. The highest incidences were reported in Baltic states, Nordic countries and Western Europe [2,3]. This work aims to provide a prognostic model for LB in the Baltic states as part of the forecasting and early warning system of the AURORA project [4].
METHODS:
Surveillance data, reporting the number of cases per month of LB in Vilnius (Lithuania), Riga (Latvia), and Tallinn (Estonia) and meteorological data were used to conduct the epidemiological modelling. The availability of historical data varied across countries: the analysis included data from 2010-2024 for Vilnius, 2006-2023 for Riga, and 1993-2019 for Tallinn. Negative binomial regression was used to predict the monthly LB incidence.
RESULTS:
The model for Vilnius data showed that temperature (°C), relative humidity (%), precipitation (mm), and atmospheric pressure (hPa) were significantly associated with monthly LB morbidity (p<0.05), with regression coefficients (B) of 0.137, 0.074, -0.148, and 0.04, respectively. In Riga, the monthly number of LB cases showed a significant positive relationship with temperature (B=0.144, p=0.001) and humidity (B=0.061, p=0.001). However, average wind speed (m/s) had a negative effect, with a one-unit increase corresponding to a 57.3% reduction in cases. The Tallinn model revealed similar
RESULTS:
the temperature and relative humidity coefficients showed a significant positive relationship with the monthly LB cases, with coefficients of 0.111 and 0.078, respectively; average wind speed had a negative effect (B=-0.785, p=0.001).
CONCLUSIONS:
Higher temperatures and increased humidity increase the prevalence of the LB across the Baltic states, while wind reduces transmission, highlighting the need for climate-based monitoring, control, and early warning systems.
| eISSN: | 2654-1459 |
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