Landscape-mediated spread dynamics and anticipatory surveillance of African swine fever in wild boar: insights from the 2025-2026 Catalonia utbreak in Spain

African swine fever (ASF) in wild boar poses major surveillance and control challenges, particularly in periurban and human-modified landscapes where ecological complexity, delayed detection, and heterogeneous host movements complicate out-break interpretation. In late 2025, ASF was detected in wild boar in Catalonia, Spain, creating a high-priority epidemiological scenario at the wildlife-urban interface. In this study, we analysed the 2025-2026 Catalonia outbreak using scenario-based spatial modelling with the WIMBOARD (Wild Integrated Movement Boar Outbreak and Risk Dynamics) framework. Simulations under baseline control conditions were used to interpret spread dynamics and support anticipatory surveillance. The results indicate that outbreak expansion was structured and directional rather than isotropic. Temporal outputs suggested delayed detectability between infection prevalence and mortality signals, whereas cumulative spatial risk maps, time-to-infection surfaces, and monthly infection-risk dynamics identified directional asymmetries and differentiated phases of spread. An early southward dispersal signal was consistent with initial field observations, while the north-northwest sector emerged as the most consequential expansion scenario because of its stronger functional connectivity and higher potential for regional amplification and persistence, while a later northeast-ward phase remained comparatively less influential within the 500-day simulation horizon. Monthly risk surfaces further showed that ASF spread behaved as a moving eco-epidemiological wavefront, allowing identification of shifting surveillance windows before mortality became apparent. These findings support the interpretation of the Catalonia outbreak as a landscape-mediated epidemiological process shaped by functional connectivity, wildlife behavioural adaptation, peri-urban ecological structure, and delayed detection. This study shows how spatially explicit eco-epidemiological modelling can support outbreak interpretation, surveillance prioritisation, and proactive wildlife disease management under complex field conditions.