AI-Powered Early Warning Systems for Zoonotic Disease Surveillance through Animal Environmental Indicators

Abstract

The increasing frequency of zoonotic disease outbreaks poses significant challenges to global public health, livestock production, and ecosystem sustainability. Conventional surveillance systems are primarily reactive, relying on laboratory-confirmed diagnoses and delayed epidemiological reporting, which often limits timely intervention and outbreak prevention. This study proposes an AI-powered early warning framework for zoonotic disease surveillance that utilizes animal environmental indicators to enable proactive disease detection under the One Health paradigm. The proposed framework integrates heterogeneous data from animal health monitoring systems, IoT sensors, remote sensing platforms, climate observations, wildlife surveillance, and epidemiological databases into a unified analytical architecture. Advanced machine learning and deep learning algorithms are employed to perform data preprocessing, feature engineering, disease risk prediction, spatiotemporal hotspot detection, and automated alert generation. The framework further incorporates GIS-based visualization and intelligent decision support to assist veterinary and public health authorities in implementing timely intervention strategies. Performance evaluation demonstrates that the proposed framework achieves a disease prediction accuracy of 96.4%, hotspot detection accuracy of 96.2%, reduces outbreak detection time from 72 hours to 12 hours, lowers the false alarm rate from 12.8% to 3.1%, and decreases response time from 180 minutes to 15 minutes compared with conventional surveillance methods. These results demonstrate that integrating AI with environmental and animal health indicators significantly enhances the accuracy, scalability, and responsiveness of zoonotic disease surveillance, providing an effective solution for early outbreak prevention and improved global health preparedness.