Lee, Da Hye; Kim, Youn Su; Koh, Young Youp; Song, Kwang Yoon; Chang, In Hong
Healthcare (Basel, Switzerland)
2021Mar ; 9 ( 3 ) :.
PMID : 33804380
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Lee, Da Hye - Department of Computer Science and Statistics, Chosun University, Gwangju, 61452, Korea.
Kim, Youn Su - Department of Computer Science and Statistics, Chosun University, Gwangju, 61452,
Koh, Young Youp - Department of Internal Medicine, College of Medicine and Medical School, Chosun
Song, Kwang Yoon - Department of Computer Science and Statistics, Chosun University, Gwangju, 61452,
Chang, In Hong - Department of Computer Science and Statistics, Chosun University, Gwangju, 61452,
ABSTRACT
From November to December 2020, the third wave of COVID-19 cases in Korea is ongoing. The government increased Seoul's social distancing to the 2.5 level, and the number of confirmed cases is increasing daily. Due to a shortage of hospital beds, treatment is difficult. Furthermore, gatherings at the end of the year and the beginning of next year are expected to worsen the effects. The purpose of this paper is to emphasize the importance of prediction timing rather than prediction of the number of confirmed cases. Thus, in this study, five groups were set according to minimum, maximum, and high variability. Through empirical data analysis, the groups were subdivided into a total of 19 cases. The cumulative number of COVID-19 confirmed cases is predicted using the auto regressive integrated moving average (ARIMA) model and compared with the actual number of confirmed cases. Through group and case-by-case prediction, forecasts can accurately determine decreasing and increasing trends. To prevent further spread of COVID-19, urgent and strong government restrictions are needed. This study will help the government and the Korea Disease Control and Prevention Agency (KDCA) to respond systematically to a future surge in confirmed cases.
keyword
ARIMA; COVID-19; confirmed cases; forecasting; pandemic; time-series
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