Spatial Modelling of Environmental Risk Factors Influencing Schmallenberg Virus Exposure in German Sheep

Area of Science:

• Veterinary Epidemiology
• Arbovirology
• Spatial Analysis

Background:

• Schmallenberg virus (SBV), a Culicoides-borne Orthobunyavirus, causes significant economic losses and reproductive issues in ruminants across Europe.
• Understanding SBV transmission drivers is crucial, especially in intensively managed livestock systems where environmental influences are poorly defined.
• Assessing spatial variation in SBV seroprevalence requires investigating landscape, climate, and host availability in endemic temperate regions.

Purpose of the Study:

• To investigate the influence of landscape, climate, and host availability on SBV exposure risk in German sheep flocks.
• To apply spatially explicit generalized additive models (GAMs) and predictive risk mapping to identify key transmission drivers.
• To determine the extent to which environmental factors explain SBV seroprevalence in a temperate, intensively managed setting.

Main Methods:

• Utilized serological data from 70 sheep flocks (2723 animals) across five German federal states (autumn 2017-spring 2018).
• Employed spatially explicit generalized additive models (GAMs) incorporating 69 environmental variables.
• Conducted predictive risk mapping to visualize spatial variation in SBV exposure risk.

Main Results:

• The final GAM explained 50.6% of the deviance in SBV seroprevalence.
• Cattle density was the strongest positive predictor (OR=1.01, p<0.001).
• Nature reserve coverage (OR=0.13, p=0.015) and summer temperature during the wettest quarter (OR=0.95, p=0.021) showed negative associations. No significant spatial clustering was detected.

Conclusions:

• Farm-level factors, particularly cattle density and associated vector habitats, are more critical for SBV transmission than broad climatic or land use gradients in Germany.
• The diffuse spatial pattern indicates a general vulnerability of German ruminants to Culicoides-borne viruses.
• Findings support targeted surveillance and farm-focused vector control strategies, with the model framework aiding future arbovirus risk assessments.