Zoonotic threats emerge across linked human, domestic-animal, wildlife, and environmental systems, yet surveillance remains frequently partitioned by sector, data structure, laboratory pathway, and authority. This integrative review examines how veterinary surveillance can contribute to earlier and more interpretable warning when signals must move across those boundaries. Evidence was selected from peer-reviewed literature published during 2017–2025 using an explicit integrative strategy that separated empirical surveillance evaluations, system descriptions, methodological frameworks, governance evidence, and review-level synthesis. The search identified 79 records; after removal of 12 duplicates, 67 were screened, 52 underwent full-record assessment, and 37 evidence units were included. Across the literature, the principal recurring problem was not absence of surveillance data alone but failure to connect signal generation with interoperable data structures, verification, contextual interpretation, and coordinated decisions. Animal-health, wildlife, environmental, laboratory/genomic, and digital streams each capture different facets of risk and differ in ascertainment, timeliness, representativeness, and transferability. Their combination therefore cannot be assumed to improve warning merely by increasing data volume. The review develops a proposed distinction between signal availability, signal interpretability, verified warning, and actionable cross-sector response. It also identifies governance, shared data semantics, laboratory linkage, feedback, and reassessment as necessary system properties for testing this distinction. The synthesis is bounded by heterogeneous study designs, uneven geographical representation, variable surveillance maturity, and limited prospective evaluation of integrated architectures. Consequently, the proposed model should be treated as an organizing framework for comparative validation rather than an implementation-ready standard.
