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Correspondence to Author: Hachung Yoon, Kyung-Sook Kim, Youngmin Son. 

Veterinary Epidemiology Division, Animal and Plant Quarantine Agency, Republic of Korea.

DOI: 10.52338/jovsr.2024.4968

Abstract:

This study presents an integrated framework for predicting and managing animal disease outbreaks in Korea using artificial intelligence (AI) models powered by big data from the Korea Animal Health Integrated System (KAHIS). First introduced during the 2016–2017 highly pathogenic avian influenza (HPAI) epidemic, these deep learning-based models estimate transmission risk by analyzing livestock vehicle movement patterns. The system produces farm- and municipality-level risk scores to guide targeted interventions for transboundary animal diseases such as HPAI, foot-and-mouth disease (FMD), African swine fever (ASF), and lumpy skin disease (LSD). While the models have proven effective in early detection and response, they are constrained by limited outbreak samples, outdated datasets, and the exclusion of environmental and vector-borne transmission variables. To enhance predictive capacity and disease preparedness, future models should incorporate multivariable data inputs and remain adaptable to evolving epidemiological landscapes.

Keywords:Artificial intelligence, transboundary animal disease, KAHIS, big data, Korea.

Citation:

Hachung Yoon, Enhancing AI-Based Risk Prediction for Animal Disease Outbreaks in Korea Using KAHIS Big Data. Journal of Veterinary Science and Research 2025.