Animal Infectious Diseases Prevention through Big Data and Deep Learning

빅데이터와 딥러닝을 활용한 동물 감염병 확산 차단

  • Kim, Sung Hyun (Big Data Project Team, Department of Big Data, National Information Society Agency) ;
  • Choi, Joon Ki (Platform Business Planning Office, BigData Business Unit, KT) ;
  • Kim, Jae Seok (Platform Business Planning Office, BigData Business Unit, KT) ;
  • Jang, Ah Reum (Platform Business Planning Office, BigData Business Unit, KT) ;
  • Lee, Jae Ho (Platform Business Planning Office, BigData Business Unit, KT) ;
  • Cha, Kyung Jin (Department of Business Administration, Kangwon National University) ;
  • Lee, Sang Won (Department of Computer & Engineering, Wonkwang University)
  • Received : 2018.10.18
  • Accepted : 2018.12.17
  • Published : 2018.12.31


Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

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Current Status of World FMD (2013)

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Current Status of Worldwide Human Damage Caused by HPAI (2003 ~ 2013)

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Propagation Path of Infectious Disease (animal vs. person)

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KAHIS System Concept

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Data Analysis Process

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2015 Prediction Model

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ROC Curve for Marchine Learning Model

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Average Error Curve for Neural Net

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Risk Comment Screen Shot

Animal Infection Disease Occurrence

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Data Status and Variable list in 2016 Model

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2016 Diffusion Risk Modeling Status and Results

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AI Outbreak Count and Statistics

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Machine Learning Model Result

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Confusion Matrix

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Summary of Model Evaluation

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Supported by : 과학기술정보통신부


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