Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study

Objective The COVID-19 outbreak was first reported in Wuhan, China, and has been acknowledged as a pandemic due to its rapid spread worldwide. Predicting the trend of COVID-19 is of great significance for its prevention. A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA. Design Time-series study. Setting The USA was the setting for this study. Main outcome measures Three accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models. Results In our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model. Conclusions The XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.

Automated summary

This study compared the accuracy of the ARIMA model and the XGBoost model in predicting the occurrence of COVID-19 in the USA. The results showed that the XGBoost model had lower MAE, RMSE, and MAPE values than the ARIMA model.