Qiqi Zhou , Shiqiang Huang

Tropical Animal Resources Research Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572025, Hainan, China
Author    Correspondence author
Computational Molecular Biology, 2025, Vol. 15, No. 6   
Received: 17 Oct., 2025    Accepted: 27 Nov., 2025    Published: 16 Dec., 2025

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.

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.

Animal epidemic; Genomic monitoring; Artificial intelligence; Epidemic prediction; Machine learning