Computational Molecular Biology 2025, Vol.15, No.6, 291-298 http://bioscipublisher.com/index.php/cmb 291 Research Insight Open Access AI-Powered Prediction of Animal Disease Outbreaks Using Genomic Surveillance Data Qiqi Zhou, Shiqiang Huang Tropical Animal Resources Research Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572025, Hainan, China Corresponding author: shiqiang.huang@hitar.org Computational Molecular Biology, 2025, Vol.15, No.6 doi: 10.5376/cmb.2025.15.0029 Received: 17 Oct., 2025 Accepted: 27 Nov., 2025 Published: 16 Dec., 2025 Copyright © 2025 Zhou and Huang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhou Q.Q., and Huang S.Q., 2025, AI-powered prediction of animal disease outbreaks using genomic surveillance data, Computational Molecular Biology, 15(6): 291-298 (doi: 10.5376/cmb.2025.15.0029) Abstract The outbreak of animal epidemics poses a significant threat to livestock production, public health security and global food supply. Traditional epidemic surveillance methods have problems such as delayed response and low resolution of pathogen recognition. With the rapid development of genomics and artificial intelligence technologies, the integration of genomic monitoring data and AI models has provided a brand-new path for the early and high-precision prediction of animal epidemics. This study reviews the main approaches for collecting genomic data of animal diseases, including whole-genome sequencing (WGS) of pathogens, metagenomics and metagenomic analysis, etc. It systematically explores the AI algorithm systems used for epidemic modeling, such as supervised learning, deep learning and graph neural networks, with a focus on analyzing their advantages in temporal pattern recognition and spatial transmission path modeling. Through the case analysis of the avian influenza epidemic, this study constructed a high-resolution genomic monitoring dataset and combined feature engineering with model comparison and evaluation to verify the superiority of the AI model in prediction accuracy and response speed. This study demonstrates the huge potential of AI-enabled genomic monitoring technology in the proactive management of animal health, providing rapid response support for emerging epidemics and promoting the construction of an intelligent and data-driven animal disease prevention and control system. Keywords Animal epidemic; Genomic monitoring; Artificial intelligence; Epidemic prediction; Machine learning 1 Introduction Before discussing animal diseases, people often first think of the troubles they bring to livestock production, but the problems usually do not end there. Some pathogens can also spread across species. Once they enter the human population, they may evolve into a larger public health event. The outbreak of the epidemic is often accompanied by large-scale culling and trade stagnation, and its impact on the agricultural economy often lasts longer. What is more difficult is that the manifestations of such diseases are not determined by a single factor. The environment, genetic background and human activities may all make the transmission more complex and increase the difficulty of early detection and control (Yoon et al., 2025). Meanwhile, the progress of high-throughput sequencing technology enables researchers to track the changes of pathogens more clearly, whether it is the evolutionary path or the transmission chain, which is more transparent than before. The integration of artificial intelligence - including machine learning and protein language models - has further enhanced the efficiency of data utilization, enabling this genomic information to predict epidemics faster and more accurately than traditional methods. Today's AI models usually analyze genomic, environmental indicators and epidemiological data together in order to assess risks more timely and assist in formulating early intervention strategies (Zhao et al., 2024; Lytras et al., 2025; Shafi et al., 2025). This study will review the challenges currently faced in disease prediction, introduce the integration methods of artificial intelligence and genomics, and explore their impact on future monitoring systems. This study aims to develop an AI-based framework that uses genomic monitoring data to predict animal disease outbreaks. By integrating multivariable inputs and adaptive learning capabilities, it improves existing models to promote proactive disease management, reduce economic losses, and lower public health risks associated with zoonoses.
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