Integrating AI, Machine Learning, and Geospatial Technologies for One Health Management of Emerging Zoonoses: Distributed Surveillance and Communication Solutions for Antimicrobial Resistance and Food Safety

The increasing prevalence of emerging zoonotic diseases, antimicrobial resistance (AMR), and food safety threats requires integrated approaches aligned with the One Health framework. This study evaluates the application of Artificial Intelligence (AI), Machine Learning (ML), and Geospatial Technologies to strengthen distributed surveillance and predictive health management across human, animal, and environmental domains. Multi-source datasets collected between 2021 and 2025 from veterinary health records, environmental monitoring systems, agricultural reports, antimicrobial usage databases, and public health surveillance platforms in selected East African regions were analyzed.

A hybrid analytical framework combining machine learning algorithms, geospatial hotspot analysis, and distributed communication-enabled surveillance systems was developed to detect zoonotic disease patterns, identify AMR risk clusters, and monitor food contamination pathways. Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) models were applied for predictive risk assessment, while Geographic Information Systems (GIS) techniques supported outbreak mapping and spatial correlation analysis. Distributed surveillance networks integrated heterogeneous clinical, environmental, and agricultural data streams for near real-time situational awareness.

Results indicate that the proposed framework significantly improved zoonotic outbreak prediction and AMR hotspot identification. The Random Forest model achieved 91.3% classification accuracy in outbreak detection, while geospatial analysis successfully identified high-risk regions associated with intensive livestock and agricultural activities. The integrated system also enhanced inter-agency communication, food safety traceability, and antimicrobial stewardship through data-driven monitoring and anomaly detection.

The study demonstrates that integrating AI, ML, geospatial intelligence, and distributed communication systems within a One Health framework provides a scalable and adaptive solution for managing emerging zoonoses, mitigating AMR risks, and improving food safety governance in resource-constrained environments.