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

  • Authors

    • 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.



  • References

    1. [1] Laney D. 2001. 3D Data Management: Controlling Data Volume, Velocity and Variety, Gartner.

      [2] Gandomi, A. and Haider, M. 2015. Beyond the Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management. 35, 2 (2015), 137-144. DOI=

      [3] Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A. H. 2011. Big Data: The Next Frontier for Innovation, Competition, and Productivity. (2011) McKinsey Global Institute

      [4] NRI, “The Advent of Big Data Eraâ€, Nomura Research Institute, 2012.

      [5] MSIP, "Big Data Industry Development Strategy", The Republic of Korea Government, 2013.

      [6] Chen, H., Chiang, R. H. L. and Storey, V. C. 2012. Business Intelligence and Analytics: From Big Data to Big Impact. MIS Q. 36, 4 (2012), 1165-1188.

      [7] Syed, A., Gillela, K. and Venugopal, C. 2013. The Future Revolution on Big Data. International Journal of Advanced Research in Computer and Communication Engineering. 2, 6 (2013), 2446-2451.

      [8] Lee, S. W. and Kim, S. H. 2016. Finding Industries for Big Data Usage on the Basis of Ahp. Journal of Digital Convergence. 14, 7 (2016), 21-27.

      [9] Shin, K. S., Chai, S. M., Park, H. J., Jo, N. O., Shin, S. A. and Kim, S. H. 2016. Development of a Big Data Capability Assessment Model. Journal of Information Technology and Architecture. 13, 2 (2016), 271-280.

      [10] Kim, S. H., Park, S. B. and Lee, Y. G. 2015. A Development of a Evaluation Framework for Public Sector Ict Adoption: Focused on Big Data, Cloud, Internet of Things. Journal of Information Technology and Architecture. 12, 3 (2015), 419-428.

      [11] N. Ashish, V. Dan, "Worldwide Big Data Technology and Services Forecast 2015-2019", IDC, 2015.

      [12] NIA, 2016 Korean Big Data Market Review, National Information Society Agency, 2017.

      [13] Braden, C. R., Dowell, S. F., Jernigan, D. B. and Hughes, J. M. 2013. Progress in Global Surveillance and Response Capacity 10 Years after Severe Acute Respiratory Syndrome. Emerging infectious diseases. 19, 6 (2013), 864.

      [14] Riley, S., Fraser, C., Donnelly, C. A., Ghani, A. C., Abu-Raddad, L. J., Hedley, A. J., Leung, G. M., Ho, L.-M., Lam, T.-H. and Thach, T. Q. 2003. Transmission Dynamics of the Etiological Agent of Sars in Hong Kong: Impact of Public Health Interventions. Science. 300, 5627 (2003), 1961-1966.

      [15] Baize, S., Pannetier, D., Oestereich, L., Rieger, T., Koivogui, L., Magassouba, N. F., Soropogui, B., Sow, M. S., Keïta, S. and De Clerck, H. 2014. Emergence of Zaire Ebola Virus Disease in Guinea. New England Journal of Medicine. 371, 15 (2014), 1418-1425.

      [16] WHO, 2015a. Middle East respiratory syndrome coronavirus (MERS-CoV)-Republic of Korea. Disease Outbreak News. 24 May 2015.

      [17] Cowling, B. J., Park, M., Fang, V. J., Wu, P., Leung, G. M. and Wu, J. T. 2015. Preliminary Epidemiologic Assessment of Mers-Cov Outbreak in South Korea, May–June 2015. Euro surveillance: bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin. 20, 25 (2015)

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    KIM, S., HWANG, H., LEE, J., CHOI, J., KANG, J., LEE, S., & ., . (2018). Design of Prevention Method Against Infectious Diseases based on Mobile Big Data and Rule to Select Subjects Using Artificial Intelligence Concept. International Journal of Engineering & Technology, 7(3.33), 174-178.