African swine fever (ASF) is a highly contagious viral disease with significant impacts on domestic pigs and wild boar populations. This study applies GIS-based spatial analysis to monitor ASF outbreaks in northwestern Italy (Piedmont and Liguria) and identify areas at increased risk. Key factors considered include pig density, wildlife proximity, and environmental conditions. The spatial analysis revealed that central–western municipalities exhibited higher risk due to favorable environmental conditions and dense wild boar populations, while peripheral areas showed a temporal delay in outbreak emergence. Mapping the spreading rate and habitat interfaces allowed the development of a spatial risk model, which was further analyzed using geostatistical techniques to understand disease dynamics. The results demonstrate the effectiveness of geospatial modeling in identifying high-risk zones, characterizing spatio-temporal patterns, and supporting targeted prevention and surveillance strategies. These findings provide actionable insights for ASF management and resource allocation. Future studies may refine these models by integrating additional datasets and environmental variables, enhancing predictive capacity and applicability across different regions.

Spatio-Temporal Pattern Analysis of African Swine Fever Spreading in Northwestern Italy—The Role of Habitat Interfaces

De Petris, Samuele;Orusa, Tommaso;Viani, Annalisa;Feliziani, Francesco;Zoppi, Simona;Ragionieri, Marco;Borgogno-Mondino, Enrico
2025-01-01

Abstract

African swine fever (ASF) is a highly contagious viral disease with significant impacts on domestic pigs and wild boar populations. This study applies GIS-based spatial analysis to monitor ASF outbreaks in northwestern Italy (Piedmont and Liguria) and identify areas at increased risk. Key factors considered include pig density, wildlife proximity, and environmental conditions. The spatial analysis revealed that central–western municipalities exhibited higher risk due to favorable environmental conditions and dense wild boar populations, while peripheral areas showed a temporal delay in outbreak emergence. Mapping the spreading rate and habitat interfaces allowed the development of a spatial risk model, which was further analyzed using geostatistical techniques to understand disease dynamics. The results demonstrate the effectiveness of geospatial modeling in identifying high-risk zones, characterizing spatio-temporal patterns, and supporting targeted prevention and surveillance strategies. These findings provide actionable insights for ASF management and resource allocation. Future studies may refine these models by integrating additional datasets and environmental variables, enhancing predictive capacity and applicability across different regions.
2025
2025
2025
2025
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African Swine Fever (ASF); GIS; Piedmont and Liguria (NW Italy); farms; geostatistics; habitat interfaces; land cover; risk assessment; spatio-temporal modeling; wildlife
De Petris, Samuele; Orusa, Tommaso; Viani, Annalisa; Feliziani, Francesco; Sordilli, Marco; Troisi, Sabatino; Zoppi, Simona; Ragionieri, Marco; Orusa,...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2102512
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