Design of Prevention Method Against Infectious Diseases based on Mobile Big Data and Rule to Select Subjects Using Artificial Intelligence Concept


  • Sunghyun KIM
  • Hyunseok HWANG
  • Jaeho LEE
  • Joonki CHOI
  • Jangmook KANG
  • Sangwon LEE
  • . .





AI, Big Data, Disease Management, Infectious Diseases, Prevention Method, Telecommunication


The rapid evolution of transportation has enabled us to travel to any part of the world. At the same time, infectious diseases are now able to reach anywhere in the world via this same ease of travel. In 2015, a MERS epidemic broke out in Korea. The MERS virus infected 186 people with 38 dead and caused economic damage worth 6 billion dollars. In this paper, we investigate a joint project of KT and KCDC (Korea Centers for Disease Control and Prevention) to prevent infectious diseases through the use of mobile roaming Big Data. KT developed a monitoring system that uses telephone roaming data to identify subscribers who traveled to a country affected with an infectious disease. Upon returning to Korea, a subscriber receives a notice that he/she is required to report for potential quarantine in accordance with the regulations and gets guided on the measures to take in case symptoms of the infectious disease occur. The travel information is also provided to healthcare facilities throughout the country for reference when symptomatic individuals visit them. Laws and regulations are enacted to allow personal information to be used to prevent and control infectious diseases. By providing a solution for monitoring individuals with potential risk of having been infected, loss of life and financial loss caused by the spread of panic of infectious disease have been minimized. This study is an example of the significant social contribution of Big Data. The global propagation of this system can reduce the threat of the spread of infection significantly.




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