The COVID-19 epidemic has spread to more than 200 countries and considered as an unprecedented public health crisis, which seriously affect people's daily life In order to find out a better way to predict and forecast the epidemic situation, this paper utilized machine learning and a series of regressions, including linear regression, polynomial regression, multi-regression, and Lasso regression models In addition, in order to improve the accuracy, this paper focused on the cumulative features With employing the algorithm mentioned above, this paper tried to find out the model that fits the training data by calculating the mean square error (MSE) or coefficient of determination (R}}2) to evaluate the models The data were divided into three temporal portions and fit into the model to make the result more accurate What's more, for states data, they are divided into different categories and added with the Time-Lag matrix The features are selected at the same time The result demonstrates that multi-linear regression and multi-polynomial regression are considerable suitable to fit the model and to predict the COVID-19 epidemic We hope our findings can serve as a valuable reference to forecast the trend of the COVID-19 epidemic situation © 2020 IEEE
The Prediction of the Spread of COVID-19 using Regression Models