CLIMATE-DRIVEN ZOONOTIC SPILLOVER SURVEILLANCE: INTEGRATING ENVIRONMENTAL MONITORING, WILDLIFE HEALTH DATA, AND FEDERATED AI FOR PRE-PANDEMIC EARLY WARNING AT HUMAN-ANIMAL INTERFACES

Abstract

Approximately 60 percent of known infectious diseases are zoonotic in origin, and the accelerating drivers of zoonotic spillover risk, including deforestation, climate-induced species range shifts, habitat fragmentation, and expanding human encroachment into wildlife habitat, are measurable weeks to months before the first human cases of a novel pathogen are detected by conventional clinical surveillance systems. This paper proposes ZooFed, a federated AI framework for pre-pandemic early warning that moves the surveillance window upstream of human case emergence by integrating five heterogeneous One Health data streams: climate and environmental change signals, wildlife health surveillance, animal reservoir pathogen genomics, interface community syndromic data, and land-use change monitoring. The framework employs a federated learning architecture that enables cross-border integration of these sensitive ecological and health datasets without requiring raw data to leave national or institutional boundaries, addressing the sovereignty and privacy constraints that prevent centralized One Health surveillance at the global scale the problem demands. A cross-domain risk model combining ecological, genomic, and epidemiological inputs generates spatiotemporal spillover risk hotspot maps, zoonotic variant tracking alerts, and intervention guidance targeted at identified high-risk interface zones. Empirical evidence from deployed multimodal federated surveillance systems demonstrates that integrating environmental data streams alongside clinical, genomic, mobility, and social signals achieves substantial detection lead times over conventional surveillance, motivating the extension of this integration approach to the pre-spillover ecological domain. The framework is evaluated against six existing surveillance systems across seven dimensions, demonstrating that ZooFed is the first system to simultaneously achieve full One Health multistream integration, federated privacy-preserving architecture, and pre-spillover detection capability.