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...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2022-07-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/7/e056685.full |
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| author | Wei Wu Zheng-gang Fang Shu-qin Yang Cai-xia Lv Shu-yi An |
| author_facet | Wei Wu Zheng-gang Fang Shu-qin Yang Cai-xia Lv Shu-yi An |
| author_sort | Wei Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b4d2efa2671b4b459b6f8ed42874437d |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-b4d2efa2671b4b459b6f8ed42874437d2025-01-31T18:40:10ZengBMJ Publishing GroupBMJ Open2044-60552022-07-0112710.1136/bmjopen-2021-056685Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series studyWei Wu0Zheng-gang Fang1Shu-qin Yang2Cai-xia Lv3Shu-yi An47 Department of Neurology, Qilu Hospital of Shandong University, Jinan, Shandong, China1 Department of Epidemiology, China Medical University, Shenyang, China1 Department of Epidemiology, China Medical University, Shenyang, China1 Department of Epidemiology, China Medical University, Shenyang, China2 Department of Social Medicine and Health, Liaoning Provincial Center for Disease Control and Prevention, Shenyang, ChinaObjective 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.https://bmjopen.bmj.com/content/12/7/e056685.full |
| spellingShingle | Wei Wu Zheng-gang Fang Shu-qin Yang Cai-xia Lv Shu-yi An Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study BMJ Open |
| title | Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study |
| title_full | Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study |
| title_fullStr | Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study |
| title_full_unstemmed | Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study |
| title_short | Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study |
| title_sort | application of a data driven xgboost model for the prediction of covid 19 in the usa a time series study |
| url | https://bmjopen.bmj.com/content/12/7/e056685.full |
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