Model of Risk of Exposure to Lyme Borreliosis and Tick-Borne Encephalitis Virus-Infected Ticks in the Border Area of the Czech Republic (South Bohemia) and Germany (Lower Bavaria and Upper Palatinate)

Int J Environ Res Public Health. 2019 Apr 2;16(7):1173. doi: 10.3390/ijerph16071173.


In Europe, Lyme borreliosis (LB) and tick-borne encephalitis (TBE) are the two vector-borne diseases with the largest impact on human health. Based on data on the density of host-seeking Ixodes ricinus ticks and pathogen prevalence and using a variety of environmental data, we have created an acarological risk model for a region where both diseases are endemic (Czech Republic-South Bohemia and Germany-Lower Bavaria, Upper Palatinate). The data on tick density were acquired by flagging 50 sampling sites three times in a single season. Prevalence of the causative agents of LB and TBE was determined. Data on environmental variables (e.g., altitude, vegetation cover, NDVI, land surface temperature) were obtained from various sources and processed using geographical information systems. Generalized linear models were used to estimate tick density, probability of tick infection, and density of infected ticks for the whole area. A significantly higher incidence of human TBE cases was recorded in South Bohemia compared to Bavarian regions, which correlated with a lower tick density in Bavaria. However, the differences in pathogen prevalence rates were not significant. The model outputs were made available to the public in the form of risk maps, indicating the distribution of tick-borne disease risk in space.

Keywords: Ixodes ricinus; Lyme borreliosis; geographical information systems; risk modeling; tick; tick-borne encephalitis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Altitude
  • Animals
  • Czech Republic / epidemiology
  • Encephalitis Viruses, Tick-Borne
  • Encephalitis, Tick-Borne / epidemiology*
  • Europe
  • Geographic Information Systems
  • Germany / epidemiology
  • Humans
  • Incidence
  • Ixodes / microbiology
  • Lyme Disease / epidemiology*
  • Prevalence
  • Probability
  • Seasons
  • Temperature