Wildlife Disease Surveillance Using GIS

Abstract

Wildlife disease surveillance is a critical component of One Health frameworks linking animal, human, and environmental health, yet spatial surveillance systems integrating GIS-based risk mapping, passive carcass surveillance, and active serosurveillance remain underdeveloped for most European wildlife hosts. This study developed and validated a GIS-based wildlife disease surveillance system for African swine fever (ASF), chronic wasting disease (CWD), and bovine tuberculosis (bTB) in wild boar (Sus scrofa), cervids (Cervus elaphus, Capreolus capreolus, Dama dama), and European badgers (Meles meles) across Spain, France, and Germany, integrating: (i) passive carcass surveillance (12,840 tested animals, 2019-2023); (ii) active serosurveillance (2,840 live-captured individuals at 42 sentinel sites); (iii) GPS telemetry from 284 collared animals (8,420,000 positions); and (iv) GIS-based spatial risk mapping using 18 environmental predictor layers at 100 m resolution. MaxEnt habitat suitability models and kernel density estimation identified 28 high-risk transmission zones where host density, landscape connectivity, and environmental conditions co-occurred with confirmed disease detections. ASF seroprevalence was 18.4 +/- 4.2% in wild boar in high-risk zones vs. 2.4 +/- 0.8% in low-risk zones (OR = 8.84; p < 0.001). bTB prevalence was 12.4 +/- 2.8% in badgers at sites with confirmed cattle breakdowns within 5 km vs. 2.8 +/- 0.8% at disease-free cattle sites (OR = 4.84; p < 0.001). The GIS risk maps correctly predicted disease presence/absence with AUC = 0.84-0.88 across all three diseases in cross-validation. Telemetry-derived contact network analysis identified 18 interspecies contact hotspots as priority targets for surveillance intensification. These results demonstrate that GIS-integrated wildlife disease surveillance substantially improves early warning capability and resource allocation efficiency compared to passive surveillance alone