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KMID : 1120220200110040239
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Osong Public Health and Research Perspectives
2020 Volume.11 No. 4 p.239 ~ p.244
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Risk Assessment Program of Highly Pathogenic Avian Influenza with Deep Learning Algorithm
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Yoon Ha-Chung
Jang Ah-Reum
Jung Chung-Sik
Ko Hun-Seok
Lee Kwang-Nyeong
Lee Eune-Sub
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Abstract
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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.
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KEYWORD
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avian influenza, deep learning, risk assessment
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