Climate-sensitive animal diseases can shift before conventional surveillance registers a rise in diagnosed cases, creating a mismatch between ecological change and outbreak recognition. This article develops an original non-empirical veterinary early-warning architecture for interpreting upstream changes in weather, vector biology, host distribution, landscape, animal movement, exposure, and response capacity before they are converted into confirmed disease counts. The synthesis distinguishes climate as a disease-system modifier from climate as a sufficient cause; pathogen development from vector biology; precipitation from hydrological state; vector abundance from competence; ecological suitability from demonstrated transmission; and exposure opportunity from confirmed infection. The central contribution is a proposed architecture in which heterogeneous environmental and veterinary signals are interpreted as trajectories, concordant or discordant evidence, and decision-relevant warning states rather than as a universal numerical score. The framework is designed to preserve uncertainty and to link escalating ecological concern with proportionate surveillance and cross-sector response while maintaining explicit boundaries between evidence-supported relationships and proposed decision logic. Its utility will depend on local vectors, pathogens, host populations, production systems, movement networks, surveillance intensity, and data completeness. It does not establish prospective predictive performance, causal attribution, or intervention effectiveness. Climate-sensitive surveillance should therefore be judged by whether it detects meaningful ecological transition early enough to improve targeted observation and preparedness without converting uncertain environmental signals into premature outbreak claims.
