Ensemble Regression Models for Short-term Prediction of Confirmed COVID-19 Cases

Abstract:

Accurately predicting the number of new COVID19 cases is critical to understanding and controlling the spread of the disease as well as effectively managing scarce resources (e.g., hospital beds, ventilators). To this end, we design a regression based ensemble learning model comprising of Linear regression, Ridge, Lasso, ARIMA, and SVR that takes the previous 14 days’ data into account to predict the number of new COVID-19 cases in the short-term. The ensemble model outputs the best performance by taking into account the performance of all the models. We consider data from top 50 countries around the world that have the highest number of confirmed cases between January 21, 2020 and April 30, 2020. Our results in terms of relative percentage error show that the ensemble method provides superior prediction performance for a vast majority of these countries with less than 10% error for 5 countries and less than 40% error for 27 countries.

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