Veterinary detection of a zoonotic pathogen is often treated as an implicit indicator of human risk, yet the inferential distance between an infected animal and a consequential human-health threat is substantial. This article develops an original non-empirical One Health decision architecture for translating veterinary detection into exposure assessment, risk escalation, communication, action, and reassessment without assuming that detection alone determines public-health significance. The analysis separates pathogen characteristics, animal-host and infection context, human exposure pathways, exposure intensity and duration, human susceptibility, environmental persistence, and evidential confidence. These domains are treated as interacting but non-interchangeable determinants of actionability. The architecture further distinguishes biological plausibility from demonstrated exposure and transmission, and it preserves uncertainty when data are sparse, temporally unstable, species-specific, or derived from experimental models. Recent H5N1 infection in dairy cattle and exposed workers provides a high-resolution stress test, while broader zoonotic surveillance, risk-assessment, communication, governance, and evaluation literature supplies the general One Health foundation. The central contribution is a proposed conditional decision structure in which veterinary signals are progressively contextualized rather than converted directly into fixed risk categories. The approach is intended to support more disciplined cross-sector reasoning and clearer reassessment triggers, not to provide a validated scoring system or universal escalation threshold. Prospective testing, jurisdiction-specific adaptation, and external validation remain necessary before effectiveness or implementation claims can be made.
