African swine fever spread in wild boars shaped by landscape and population processes

Human-driven landscape modification alters wildlife movements and contact networks, generating new and often more efficient pathways for pathogen transmission. Wild boars exhibit strong adaptability to anthropogenic environments and sustain and maintain African swine fever (ASF) circulation through dense aggregations and persistent carcasses, producing considerable ecological and economic impacts. Existing individual-based models frequently underrepresent mechanistic pathways by which hosts acquire energy, interact with heterogeneous resources, and convert these dynamics into growth, movement, and transmission risk. A spatially explicit individual-based model (IBM) was developed, integrating host bioenergetics, demography, and home-range foraging and relocation with a Susceptible Exposed Infectious Dead Carcass infectious Removed/Recovered (SEIDCR) transmission module incorporating carcass persistence to predict ASF risk in wild boar populations. Incorporating behavioral processes altered epidemic trajectories, generating secondary infection waves and sustaining intermediate-phase effective reproduction numbers ( R e ), compared to a single-peaked trajectory under behavior-off scenario. This configuration improved spatial predictive performance (AUC = 0.63), with high-risk areas forming patch-like clusters consistent with observed ASF-positive carcass distributions. Embedding individual-level behavior within a population dynamic transmission framework enhanced predictive accuracy, captured recurrent epidemic waves, and delineated high-risk areas at operational management scale. This framework links landscape structure to host aggregation and contact processes, producing decision-ready risk maps to inform carcass removal, population management, and surveillance strategies. • Models ASF spread in wild boars using a stochastic individual-based approach. • Incorporates behavioral and environmental heterogeneity in transmission dynamics. • Captures epidemic waves and identifies spatial infection hotspots. • Links landscape structure to host aggregation and disease contact networks. • Supports risk mapping for targeted surveillance and carcass removal.