A Novel βSA Ensemble Model for Forecasting the Number of Confirmed COVID-19 Cases in the US

Min-Jui Yang,Ming-Hung Shih,Ting-Wei Wu,David C Yen,Dong-Her Shih

doi:10.3390/math10050824

Min-Jui Yang, Ming-Hung Shih + Show 3 more

Open Access

https://doi.org/10.3390/math10050824

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Abstract

In December 2019, Severe Special Infectious Pneumonia (SARS-CoV-2)–the novel coronavirus (COVID-19)– appeared for the first time, breaking out in Wuhan, China, and the epidemic spread quickly to the world in a very short period time. According to WHO data, ten million people have been infected, and more than one million people have died; moreover, the economy has also been severely hit. In an outbreak of an epidemic, people are concerned about the final number of infections. Therefore, effectively predicting the number of confirmed cases in the future can provide a reference for decision-makers to make decisions and avoid the spread of deadly epidemics. In recent years, the α-Sutte indicator method is an excellent predictor in short-term forecasting; however, the α-Sutte indicator uses fixed static weights. In this study, by adding an error-based dynamic weighting method, a novel β-Sutte indicator is proposed. Combined with ARIMA as an ensemble model (βSA), the forecasting of the future COVID-19 daily cumulative number of cases and the number of new cases in the US are evaluated from the experiment. The experimental results show that the forecasting accuracy of βSA proposed in this study is better than other methods in forecasting with metrics MAPE and RMSE. It proves the feasibility of adding error-based dynamic weights in the β-Sutte indicator in the area of forecasting.

Highlights

In December 2019, Severe Special Infectious Pneumonia (SARS-CoV-2)–the novel coronavirus (COVID-19)–appeared for the first time, breaking out in Wuhan City, Hubei Province, China
According to the World Health Organization (WHO), COVID-19 is an emerging disease which has the characteristics of human-to-human transmission and is extremely contagious, can infect ten million people, and has caused more than millions of deaths around the world
Many academics have invested in research on COVID-19, for example, to predict the future daily and monthly number forecasting of confirmed cases [2–4], or to discover related factors that affect the severity of the epidemic and cause death [5,6]

Summary

Introduction

In December 2019, Severe Special Infectious Pneumonia (SARS-CoV-2)–the novel coronavirus (COVID-19)–appeared for the first time, breaking out in Wuhan City, Hubei Province, China. Many academics have invested in research on COVID-19, for example, to predict the future daily and monthly number forecasting of confirmed cases [2–4], or to discover related factors that affect the severity of the epidemic and cause death [5,6]. If we can correctly predict the number of confirmed cases in the U.S, we can know the future trend of confirmed COVID-19 cases of the world. Al-Dahidi, Baraldi, Zio, and Legnani [9] have proven that adding dynamic weights to the ensemble model led to better results than the original. Since the α-Sutte indicator uses static weight in forecasting, this study attempts to add dynamic weights into the α-Sutte indicator and incorporate it with the ARIMA method to make an ensemble forecasting model.

Data Collection

The α-Sutte and

Autoregressive Integrated Moving Average (ARIMA)

One-Day-Ahead Forecasting of the Cumulative Number of Confirmed Cases