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ISSN : 2287-7991(Print)
ISSN : 2287-8009(Online)
Journal of the Preventive Veterinary Medicine Vol.41 No.4 pp.180-185

Development of mathematical model on regionalization using
records of livestock related vehicles for control strategy of
highly pathogenic avian influenza

Jonghyun Seo1,2, Hyuk Park1, Kwang-Hee Han3, Wooseog Jeong4, Hachung Yoon4, Ki-Hyun Cho4, Chung-Sik Jung4, Yong-Myung Kang4, Hong-Sik Park4, Son-Il Pak5, Hunseok Kang1,6†
1Department of Development, EZFarm LTD.
2Finance⋅Fishery⋅Manufacture Industrial Mathematics Center on Big Data, Pusan National University,
3Department of Consilience, Korea Polytechnic University,
4Veterinary Epidemiology Division, Animal and Plant Quarantine Agency
5College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University
6Department of Mathematics, American University of the Middle East, 250 St. Eqaila, State of Kuwait


In this paper, a mathematical model of regionalization based on graph theory to investigate the patterns induced by movements of livestock vehicles in cities under outbreaks of highly pathogenic avian influenza (HPAI) is proposed. We then compare the results of simulation from the regionalization model to actual HPAI outbreaks in 2016/2017 to evaluate the validity of the model. Specifically, we (1) configured a complex network structure with analytic tools and properties in graph theory to abstract the paths among farms and livestock facilities; (2) employed statistical methods to estimate the possibility of propagation between two clusters; (3) applied the developed method to an actual HPAI outbreak in Korea in 2016 and conducted a simulation to determine if the proposed modeling for regionalization is an effective prediction measure. The clustered regions proposed by the simulation correctly reflected the regional clustering of actual cases, while simultaneously contain the cities exposed to potential damage when separated. Based on these findings, we conclude that our proposed regionalization model is suitable for making policy judgments to establish a preemptive biosecurity system.