A Hybrid One Health--Community Adaptive Framework Combined with Multi-Scale SEIHRD Modeling and Bayesian Inference for Rapid Suppression of the 2026 Bundibugyo Ebolavirus Outbreak in Conflict Zones

The May 2026 emergence of the rare Bundibugyo ebolavirus (BDBV) in northeastern Democratic Republic of the Congo (DRC) and its rapid cross-border propagation into Uganda led to a Public Health Emergency of International Concern (PHEIC) declaration by the World Health Organization on May 17, 2026. This outbreak develops within a highly complex epidemiological and geopolitical landscape, characterized by active conflict, refugee displacement, and the absence of licensed vaccines or targeted therapeutics. This paper presents an innovative, highly integrated, multi-scale framework that explicitly couples zoonotic spillover dynamics and spatial human mobility across conflict-driven patches with a classical epidemiological compartmental structure (SEIHRD). By linking ecological surveillance (One Health) with bottom-up social mobilization, genomic sequencing, and humanitarian access negotiation strategies, we present a robust hybrid operational roadmap. Projections from our calibrated model demonstrate that executing this combined framework at Day 25 yields an estimated 88% reduction in overall outbreak size in the primary epicenter, successfully intercepts transboundary propagation to the secondary node, and suppresses the global reproduction number below the critical threshold (R₀ < 1). We derive the analytical basic reproduction number (R₀) under multi-patch coupling, formalize diagnostic cost-effectiveness trade-offs under severe resource limitations, and perform robust Bayesian parameter estimation using Metropolis-Hastings Markov Chain Monte Carlo (MCMC). A fully functional, self-contained Python architecture is provided to demonstrate the mathematical tractability and Popperian falsifiability of the framework, offering actionable guidelines for international response coordination.