DoYeong Jeon, Myeong Ho Song, Soo Dong Kim, Journal of Internet Computing and Services, Vol. 22, No. 1, pp. 65-77, Feb. 2021
10.7472/jksii.2021.22.1.65, Full Text:
Keywords: COVID-19, Machine Learning, clustering, Risk Analytics, Safe Activity Assistance

Abstract

COVID-19 has recently impacted the world with the large numbers of infected and deaths. The development of effective COVID-19 vaccine has not been successful. Hence, people have a high concern on the infection of this disease. The infection information from the governmantal public organizations are mainly based on simple summary statistics. Consequently, it is hard to assess the infection risks of individual person and the current location of the person. In this paper, we present a machine learning-based software system that analyzes COVID-19 infection risks and guidelines for safe activities.This paper proposes a suite of risk factors regarding COVID-19 infection and deaths and methods to quantitatively measure the individual and group risks using the proposed metrics. The proposed system utilizes a clustering algorithms and various software approaches that reflect the information and features of inviduals and their geograpical locations.

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