Decoding the Geography of Natural TBEV Microfoci in Germany: A Geostatistical Approach Based on Land-Use Patterns and Climatological Conditions

Abstract

Background: Tickborne-encephalitis (TBE) is a potentially life-threating neurological disease that is mainly transmitted by ticks. The goal of the present study is to analyze the potential uniform environmental patterns of the identified TBEV microfoci in Germany. The results are used to calculate probabilities for the present distribution of TBEV microfoci in Germany based on a geostatistical model. Methods: We aim to consider the specification of environmental characteristics of locations of TBEV microfoci detected in Germany using open access epidemiological, geographical and climatological data sources. We use a two-step geostatistical approach, where in a first step, the characteristics of a broad set of environmental variables between the 56 TBEV microfoci and a control or comparator set of 3575 sampling points covering Germany are compared using Fisher's Exact Test. In the second step, we select the most important variables, which are then used in a MaxEnt distribution model to calculate a high resolution (400 × 400 m) probability map for the presence of TBEV covering the entire area of Germany. Results: The findings from the MaxEnt prediction model indicate that multi annual actual evapotranspiration (27.0%) and multi annual hot days (22.5%) have the highest contribution to our model. These two variables are followed by four additional variables with a lower, but still important, explanatory influence: Land cover classes (19.6%), multi annual minimum air temperature (14.9%), multi annual sunshine duration (9.0%), and distance to coniferous and mixed forest border (7.0%). Conclusions: Our findings are based on defined TBEV microfoci with known histories of infection and the repeated confirmation of the virus in the last years, resulting in an in-depth high-resolution model/map of TBEV microfoci in Germany. Multi annual actual evapotranspiration (27%) and multi annual hot days (22.5%) have the most explanatory power in our model. The results may be used to tailor specific regional preventive measures and investigations.

Keywords: Ixodes ricinus; MaxEnt; TBE; TBEV; climatological data; environmental variables; geostatistical approach; land-use patterns; microfocus; prediction model; tick-borne encephalitis.

Figure 1

General overview regarding different land cover classes in Germany. Major cities are displayed, as well as the location of TBEV microfoci used in our analysis.

Figure 2

Overview regarding TBEV microfoci and the surrounding land cover classes within 400 × 400 m.

Figure 3

Environmental variables that were included in the MaxEnt model. The number of variables is step-by-step reduced to the final and most important six variables, which are used in the final run of the MaxEnt model.

Figure 4

Distribution of different land cover types around the TBEV microfoci compared with the control sampling points. The results of Fisher’s Exact Test are displayed in the inset.

Figure 5

MaxEnt predicted probabilities for Germany based on the locations of the TBEV microfoci and environmental data. The probability is displayed in different grey shades.

Figure 6

MaxEnt predicted probabilities for Germany aggregated on NUTS 3 level. The probability is displayed in different grey shades.

Figure 7

TBE incidence between 2001 and 2020 in Germany based on notified TBEV infections, aggregated on NUTS 3 level. The incidence is displayed in different red shades.

Figure 8

MaxEnt predicted probabilities correlated with TBE incidences between 2001 and 2020 in Germany, data are aggregated on NUTS 3 level. The inset shows the results of Pearson’s product–moment correlation.