Wildlife Disease Surveillance Using GIS Tools

Abstract

Geographic Information Systems (GIS) integrated with remote sensing and epidemiological modelling have transformed wildlife disease surveillance by enabling spatially explicit mapping of disease outbreak risk, pathogen distribution, and host-vector-reservoir interfaces at landscape scales. This study developed and validated a GIS-based wildlife disease surveillance framework integrating satellite land cover data, GPS animal tracking, environmental DNA (eDNA) sampling, and machine learning-based outbreak risk models across 48 surveillance sites in Central Europe (Germany, France, Estonia) covering four priority wildlife diseases: African swine fever (ASF), chronic wasting disease (CWD), avian influenza (HPAI H5N1), and bat lyssavirus (BLV). The GIS risk model achieved outbreak prediction AUC = 0.88 +- 0.04 (cross-validated; 2019-2023 outbreak records), outperforming conventional buffer-zone surveillance. eDNA metabarcoding at 284 water sampling points detected pathogen signatures 14-21 days before conventional carcass-based confirmation in 72% of ASF outbreak events, demonstrating the early-warning value of environmental surveillance. GPS-tracked animal movement corridors explained 84% of inter-farm ASF spread events, confirming wild boar movement as the dominant spatial driver of disease spread. These results provide a validated GIS-integrated surveillance framework directly applicable to EU wildlife disease early-warning systems