AEMS–One Health Bayesian Architecture Document v1.0: Probabilistic Environmental and Epidemiological Intelligence for Sudan Ebolavirus Early Warning Systems

This document defines the probabilistic architecture underlying the AEMS-One Health Early Warning System, designed for Sudan ebolavirus (SUDV) spillover prediction in data-scarce environments. The framework combines environmental intelligence, wildlife surveillance, livestock exposure pathways, and human vulnerability indicators through hierarchical Bayesian probabilistic inference. Key features include:• Hierarchical Bayesian network structure with five integrated domains• Environmental Driver Layer (flood persistence, soil moisture, forest fragmentation)• Reservoir Dynamics Layer (bat-human contact probability)• Livestock Amplification Layer (pig exposure risk)• Wildlife Sentinel Layer (early warning signals)• Human Exposure Layer (vulnerability assessment)• Explicit uncertainty quantification through probability distributions• Expert knowledge integration via informative priors• Dynamic learning process with continuous posterior updates The architecture produces posterior probability distributions rather than deterministic values, enabling risk-informed decision-making under uncertainty. This document serves as the foundational specification for the AEMS-One Health framework, targeting operational deployment in Uganda's Mubende and Kassanda districts.