Short-Term Prediction Methodology of COVID-19 Infection in South Korea

被引:2
|
作者
Ko, Grace S. [1 ]
Yoon, Taeseon [1 ]
机构
[1] Hankuk Acad Foreign Studies, Yongin 17035, Gyeonggi, South Korea
来源
COVID | 2021年 / 1卷 / 01期
关键词
COVID-19; SIR model; regression; LSTM;
D O I
10.3390/covid1010035
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
The purpose of this study is to predict the short-term trend of the COVID-19 pandemic and give insights into effective response strategies. Based on the basic SIR model, a compartment method for modeling the course of an epidemic, the short-term infection change ratio md, is derived. The number of infected people can be predicted using this ratio. We calculated different md values on a weekly basis. As we tested different combinations of md, the prediction from the combination of md based on a week and md based on 4 weeks was found to be statistically reliable. According to our regression analysis, our approach has an explanatory power of 96%. However, this method could only predict 1 week ahead of current data. Thus, we use LSTM, a deep learning method applied for time series data, to forecast the trend 4 weeks ahead. The forecasted trends show that the number of infected people in South Korea will reach its peak a week after the writing of this work and start to gradually decline after that.
引用
收藏
页码:416 / 422
页数:7
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