Osong Public Health and Research Perspectives

Hachung Yoon

3 Articles

Risk Assessment Program of Highly Pathogenic Avian Influenza with Deep Learning Algorithm: Hachung Yoon, Ah-Reum Jang, Chungsik Jung, Hunseok Ko, Kwang-Nyeong Lee, Eunesub Lee; Osong Public Health Res Perspect. 2020;11(4):239-244. Published online August 31, 2020; DOI: https://doi.org/10.24171/j.phrp.2020.11.4.13

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Objectives

This study presents the development and validation of a risk assessment program of highly pathogenic avian influenza (HPAI). This program was developed by the Korean government (Animal and Plant Quarantine Agency) and a private corporation (Korea Telecom, KT), using a national database (Korean animal health integrated system, KAHIS).

Methods

Our risk assessment program was developed using the multilayer perceptron method using R Language. HPAI outbreaks on 544 poultry farms (307 with H5N6, and 237 with H5N8) that had available visit records of livestock-related vehicles amongst the 812 HPAI outbreaks that were confirmed between January 2014 and June 2017 were involved in this study.

Results

After 140,000 iterations without drop-out, a model with 3 hidden layers and 10 nodes per layer, were selected. The activation function of the model was hyperbolic tangent. Precision and recall of the test gave F1 measures of 0.41, 0.68 and 0.51, respectively, at validation. The predicted risk values were higher for the “outbreak” (average ± SD, 0.20 ± 0.31) than “non-outbreak” (0.18 ± 0.30) farms (p < 0.001).

Conclusion

The risk assessment model developed was employed during the epidemics of 2016/2017 (pilot version) and 2017/2018 (complementary version). This risk assessment model enhanced risk management activities by enabling preemptive control measures to prevent the spread of diseases.

Citations

Citations to this article as recorded by

Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks
Emmanuel Musa, Zahra Movahhedi Nia, Nicola Luigi Bragazzi, Doris Leung, Nelson Lee, Jude Dzevela Kong
Healthcare.2024; 12(19): 1959. CrossRef
Big data-based risk assessment of poultry farms during the 2020/2021 highly pathogenic avian influenza epidemic in Korea
Hachung Yoon, Ilseob Lee, Hyeonjeong Kang, Kyung-Sook Kim, Eunesub Lee, Mathilde Richard
PLOS ONE.2022; 17(6): e0269311. CrossRef
Artificial Intelligence Models for Zoonotic Pathogens: A Survey
Nisha Pillai, Mahalingam Ramkumar, Bindu Nanduri
Microorganisms.2022; 10(10): 1911. CrossRef

H5N8 Highly Pathogenic Avian Influenza in the Republic of Korea: Epidemiology During the First Wave, from January Through July 2014: Hachung Yoon, Oun-Kyong Moon, Wooseog Jeong, Jida Choi, Young-Myong Kang, Hyo-Young Ahn, Jee-Hye Kim, Dae-Sung Yoo, Young-Jin Kwon, Woo-Seok Chang, Myeong-Soo Kim, Do-Soon Kim, Yong-Sang Kim, Yi-Seok Joo; Osong Public Health Res Perspect. 2015;6(2):106-111. Published online April 30, 2015; DOI: https://doi.org/10.1016/j.phrp.2015.01.005

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16 Crossref

Abstract PDF

Objectives
This study describes the outbreaks of H5N8 highly pathogenic avian influenza (HPAI) in Korea during the first wave, from January 16, 2014 through July 25, 2014. Its purpose is to provide a better understanding of the epidemiology of H5N8 HPAI.
Methods
Information on the outbreak farms and HPAI positive wild birds was provided by the Animal and Plant Quarantine Agency. The epidemiological investigation sheets for the outbreak farms were examined.
Results
During the 7-month outbreak period (January–July 2014), H5N8 HPAI was confirmed in 212 poultry farms, 38 specimens from wild birds (stools, birds found dead or captured). Ducks were the most frequently infected poultry species (159 outbreak farms, 75.0%), and poultry in 67 (31.6%) outbreak farms was asymptomatic.
Conclusion
As in the previous four H5N1 epidemics of HPAI that occurred in Korea, this epidemic of H5N8 proved to be associated with migratory birds. Poultry farms in Korea can hardly be free from the risk of HPAI introduced via migratory birds. The best way to overcome this geographical factor is to reinforce biosecurity to prevent exposure of farms, related people, and poultry to the pathogen.

Citations

Citations to this article as recorded by

Spatial and Temporal Characteristic Analysis and Risk Assessment of Global Highly Pathogenic Avian Influenza H5N8 Subtype
Qi An, Yuepeng Li, Zhuo Sun, Xiang Gao, Hongbin Wang, Daniel Diaz
Transboundary and Emerging Diseases.2024; 2024: 1. CrossRef
Dispensable role of wild rodents in avian influenza A virus transmission in Gyeonggi province, Korea
Chung-Young Lee, Ilhwan Kim, Hyuk-Joon Kwon
Korean Journal of Veterinary Research.2024; 64(2): e13. CrossRef
Complex Evolutionary Dynamics of H5N8 Influenza A Viruses Revealed by Comprehensive Reassortment Analysis
Egor Degtyarev, Sofia Feoktistova, Pavel Volchkov, Andrey Deviatkin
Viruses.2024; 16(9): 1405. CrossRef
The global prevalence of highly pathogenic avian influenza A (H5N8) infection in birds: A systematic review and meta-analysis
Xue-Yao Yang, Qing-Long Gong, Yan-Jin Li, Emad Beshir Ata, Man-Jie Hu, Yong-Yang Sun, Zhi-Yang Xue, Ying-Shi Yang, Xue-Pan Sun, Chun-Wei Shi, Gui-Lian Yang, Hai-Bin Huang, Yan-Long Jiang, Jian-Zhong Wang, Xin Cao, Nan Wang, Yan Zeng, Wen-Tao Yang, Chun-Fe
Microbial Pathogenesis.2023; 176: 106001. CrossRef
Impact of inland waters on highly pathogenic avian influenza outbreaks in neighboring poultry farms in South Korea
Saleem Ahmad, Kyeyoung Koh, Daesung Yoo, Gukhyun Suh, Jaeil Lee, Chang-Min Lee
Journal of Veterinary Science.2022;[Epub] CrossRef
Emergence of a Novel Reassortant H5N3 Avian Influenza Virus in Korean Mallard Ducks in 2018
Seon-Ju Yeo, Vui Thi Hoang, Tuan Bao Duong, Ngoc Minh Nguyen, Hien Thi Tuong, Mudsser Azam, Haan Woo Sung, Hyun Park
Intervirology.2022; 65(1): 1. CrossRef
Wild birds as reservoirs for diverse and abundant gamma- and deltacoronaviruses
Michelle Wille, Edward C Holmes
FEMS Microbiology Reviews.2020; 44(5): 631. CrossRef
Virus–virus interactions and host ecology are associated with RNA virome structure in wild birds
Michelle Wille, John‐Sebastian Eden, Mang Shi, Marcel Klaassen, Aeron C. Hurt, Edward C. Holmes
Molecular Ecology.2018; 27(24): 5263. CrossRef
Development of Clade-Specific and Broadly Reactive Live Attenuated Influenza Virus Vaccines against Rapidly Evolving H5 Subtype Viruses
Kobporn Boonnak, Yumiko Matsuoka, Weijia Wang, Amorsolo L. Suguitan, Zhongying Chen, Myeisha Paskel, Mariana Baz, Ian Moore, Hong Jin, Kanta Subbarao, Douglas S. Lyles
Journal of Virology.2017;[Epub] CrossRef
Multidimensional analysis model for highly pathogenic avian influenza using data cube and data mining techniques
Zhenshun Xu, Jonguk Lee, Daihee Park, Yongwha Chung
Biosystems Engineering.2017; 157: 109. CrossRef
Five distinct reassortants of H5N6 highly pathogenic avian influenza A viruses affected Japan during the winter of 2016–2017
Nobuhiro Takemae, Ryota Tsunekuni, Kirill Sharshov, Taichiro Tanikawa, Yuko Uchida, Hiroshi Ito, Kosuke Soda, Tatsufumi Usui, Ivan Sobolev, Alexander Shestopalov, Tsuyoshi Yamaguchi, Junki Mine, Toshihiro Ito, Takehiko Saito
Virology.2017; 512: 8. CrossRef
Complete analysis of the H5 hemagglutinin and N8 neuraminidase phylogenetic trees reveals that the H5N8 subtype has been produced by multiple reassortment events
Andrew R. Dalby
F1000Research.2016; 5: 2463. CrossRef
Phylogenetic and biological characterization of three K1203 (H5N8)-like avian influenza A virus reassortants in China in 2014
Juan Li, Min Gu, Dong Liu, Benqi Liu, Kaijun Jiang, Lei Zhong, Kaituo Liu, Wenqi Sun, Jiao Hu, Xiaoquan Wang, Shunlin Hu, Xiaowen Liu, Xiufan Liu
Archives of Virology.2016; 161(2): 289. CrossRef
Experimental infection of SPF and Korean native chickens with highly pathogenic avian influenza virus (H5N8)
Eun-Kyoung Lee, Byung-Min Song, Hyun-Mi Kang, Sang-Hee Woo, Gyeong-Beom Heo, Suk Chan Jung, Yong Ho Park, Youn-Jeong Lee, Jae-Hong Kim
Poultry Science.2016; 95(5): 1015. CrossRef
Wild waterfowl migration and domestic duck density shape the epidemiology of highly pathogenic H5N8 influenza in the Republic of Korea
Sarah C. Hill, Youn-Jeong Lee, Byung-Min Song, Hyun-Mi Kang, Eun-Kyoung Lee, Amanda Hanna, Marius Gilbert, Ian H. Brown, Oliver G. Pybus
Infection, Genetics and Evolution.2015; 34: 267. CrossRef
Intracontinental and intercontinental dissemination of Asian H5 highly pathogenic avian influenza virus (clade 2.3.4.4) in the winter of 2014–2015
Takehiko Saito, Taichiro Tanikawa, Yuko Uchida, Nobuhiro Takemae, Katsushi Kanehira, Ryota Tsunekuni
Reviews in Medical Virology.2015; 25(6): 388. CrossRef

Estimation of the Infection Window for the 2010/2011 Korean Foot-and-Mouth Disease Outbreak: Hachung Yoon, Soon-Seek Yoon, Han Kim, Youn-Ju Kim, Byounghan Kim, Sung-Hwan Wee; Osong Public Health Res Perspect. 2013;4(3):127-132. Published online June 30, 2013; DOI: https://doi.org/10.1016/j.phrp.2013.04.010

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18 Download
11 Crossref

Abstract PDF

Objectives
This study aims to develop a method for calculating infection time lines for disease outbreaks on farms was developed using the 2010/2011 foot-and-mouth disease (FMD) epidemic in the Republic of Korea.
Methods
Data on farm demography, the detection date of FMD, the clinical history for the manifestation of lesions, the presence of antibodies against FMD virus (including antibodies against the structural and nonstructural proteins of serotype O), vaccination status (O1 Manisa strain), the number of reactors and information on the slaughter of infected animals were utilized in this method.
Results
Based on estimates of the most likely infection date, a cumulative detection probability that an infected farm would be identified on a specific day was determined. Peak infection was observed between late December and early January, but peak detection occurred in mid-January. The early detection probability was highest for pigs, followed by cattle (dairy, then beef) and small ruminants. Nearly 90% of the infected pig farms were detected by Day 11 post-infection while 13 days were required for detection for both dairy and beef cattle farms, and 21 days were necessary for small ruminant (goat and deer) farms. On average, 8.1 ± 3.1 days passed prior to detecting the presence of FMD virus on a farm. The interval between infection and detection of FMD was inversely associated with the intensity of farming.
Conclusion
The results of our study emphasize the importance of intensive clinical inspection, which is the quickest method of detecting FMD infection and minimizing the damage caused by an epidemic.

Citations

Citations to this article as recorded by

Estimating the time of infection for African swine fever in pig farms in Korea
Hachung Yoon, Youngmin Son, Kyung-Sook Kim, Ilseob Lee, Yeon-Hee Kim, Eunesub Lee
Frontiers in Veterinary Science.2023;[Epub] CrossRef
A Meta-Population Model of Potential Foot-and-Mouth Disease Transmission, Clinical Manifestation, and Detection Within U.S. Beef Feedlots
Aurelio H. Cabezas, Michael W. Sanderson, Victoriya V. Volkova
Frontiers in Veterinary Science.2020;[Epub] CrossRef
Probabilistic assessment of potential leachate leakage from livestock mortality burial pits: A supervised classification approach using a Gaussian mixture model (GMM) fitted to a groundwater quality monitoring dataset
Hyun-Koo Kim, Kyoung-Ho Kim, Seong-Taek Yun, Junseop Oh, Ho-Rim Kim, Sun-Hwa Park, Moon-Su Kim, Tae-Seung Kim
Process Safety and Environmental Protection.2019; 129: 326. CrossRef
Using Simulated Annealing to Improve the Information Dissemination Network Structure of a Foreign Animal Disease Outbreak Response
James D. Pleuss, Jessica L. Heier Stamm, Jason D. Ellis
Journal of Homeland Security and Emergency Managem.2018;[Epub] CrossRef
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A. C. Kinsley, K. VanderWaal, M. E. Craft, R. B. Morrison, A. M. Perez
Transboundary and Emerging Diseases.2018; 65(5): 1307. CrossRef
A study on the spread of the foot-and-mouth disease in Korea in 2010/2011
Jihyun Hwang, Changhyuck Oh
Journal of the Korean Data and Information Science.2014; 25(2): 271. CrossRef
Summing Up Again
Hae-Wol Cho, Chaeshin Chu
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Prueksakorn Kritana, Kim Taehyeung, Kim Hyeontae, Kim Ki Youn, Son Wongeun
Computers and Electronics in Agriculture.2014; 108: 95. CrossRef
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