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

Grace S Ko,Taeseon Yoon

doi:10.3390/covid1010035

Abstract

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.

Highlights

Prediction Methodology ofIn December 2019, an unknown novel coronavirus, SARS-CoV-2 (COVID-19), was first reported in Wuhan, China
Data from 21 January 2020 to 10 August 2021 are used; these data are taken from the data provided by the Korea Centers for Disease Control and Prevention (KCDC) [12]
This study focuses on short-term prediction as a strategic response to COVID-19 rather than predicting the end of the COVID-19 epidemic

Summary

Introduction

In December 2019, an unknown novel coronavirus, SARS-CoV-2 (COVID-19), was first reported in Wuhan, China. As globalization has increased the frequency of personal and business travel to other countries, COVID-19 has spread rapidly around the world. On 11 March 2020, the World Health Organization (WHO) declared a pandemic [1]. 21 August 2021, a total of 211,503,434 confirmed cases of COVID-19 were reported, and the death toll reached 4,426,543 [2]. Available information about COVID-19 is being collected, especially regarding its nature and characteristics. It is known that the virus tends to change its nature, evolving new variants based on genetic mutations. Thorough research is urgent in order to find the most effective measurements that can help end the COVID-19 pandemic [3]

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Methods

Conclusion