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.2026年5月，罕见的本迪布焦埃博拉病毒（Bundibugyo ebolavirus, BDBV）在刚果民主共和国（Democratic Republic of the Congo, DRC）东北部出现，并快速跨境传播至乌干达，世界卫生组织于2026年5月17日将此次疫情列为国际关注的突发公共卫生事件（Public Health Emergency of International Concern, PHEIC）。 此次疫情暴发于高度复杂的流行病学与地缘政治环境中，当地存在持续武装冲突、难民流离失所的情况，且尚无获批疫苗或针对性治疗药物。 本研究提出一种创新性的高度集成多尺度框架，该框架将人畜共患病溢出动态、冲突驱动斑块间的空间人员流动，与经典流行病学仓室模型结构（SEIHRD）进行显式耦合。 通过将生态监测（同一健康，One Health）与自下而上的社会动员、基因组测序及人道主义准入谈判策略相结合，本研究提出一套稳健的混合行动路线图。 经校准后的模型预测显示，在疫情暴发第25日启动该联合框架，可使原发疫中地区的整体疫情规模预计降低88%，成功阻断疫情向次级传播节点的跨境传播，并将全球基本再生数（basic reproduction number, R₀）压制至临界阈值以下（R₀ < 1）。 本研究推导了多斑块耦合场景下的解析解形式基本再生数，明确了极端资源限制下诊断措施的成本效益权衡关系，并采用Metropolis-Hastings马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）方法开展稳健的贝叶斯参数估计。 本研究提供了一套完整可独立运行的Python架构，用以展示该框架的数学可解性与波普尔证伪性（Popperian falsifiability），可为国际疫情响应协调提供可落地的行动指南。