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

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 alpha-Sutte indicator method is an excellent predictor in short-term forecasting; however, the alpha-Sutte indicator uses fixed static weights. In this study, by adding an error-based dynamic weighting method, a novel beta-Sutte indicator is proposed. Combined with ARIMA as an ensemble model (beta SA), 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 beta SA 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 beta-Sutte indicator in the area of forecasting.