ArticleBMJ open2022
Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study.
Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Early adherence predicts treatment outcomes and mortality in people living with HIV and multidrug-resistant tuberculosis.Annals of epidemiology · 2026Article
- Data-Driven Machine Learning-Based Forecasting of Dengue in Bangladesh: Supporting Digital Health Approaches for Early Warning.Health science reports · 2026Article
- Designing an explainable algorithm based on XGBoost and genetic algorithm for predicting hospitalization needs of COVID-19 patients.Scientific reports · 2026Article
- Application of seasonal-adjusted hybrid models for forecasting Discomfort Index in a heat-prone region of Bangladesh.PloS one · 2026Article
- Hierarchical forecasting of COVID-19 cases in Africa using machine learning models.Frontiers in epidemiology · 2026Article
- Predictive and interpretable machine learning for COVID-19 resurgences: the role of SARS-CoV-2 variants in the post-pandemic era.BMC infectious diseases · 2025Article
- Stroke Mortality in Kazakhstan: Comparison of National Health Records to Global Burden of Disease Study.JACC. Asia · 2025Article
- Wastewater as an early indicator for short-term forecasting COVID-19 hospitalization in Germany.BMC public health · 2025Article
- Predicting outcomes following endovascular aortoiliac revascularization using machine learning.NPJ digital medicine · 2025Article
- Comparison of machine learning model performance for predicting the climate variables in Johor Bahru, Malaysia.Scientific reports · 2025Article
- Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.Scientific reports · 2025Article
- Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.Journal of the American Heart Association · 2025Article
- Using machine learning to predict outcomes following transcarotid artery revascularization.Scientific reports · 2025Article
- Residual XGBoost regression-Based individual moving range control chart for Gross Domestic Product growth monitoring.PloS one · 2025Article
- Interpretable machine learning analysis of environmental characteristics on bacillary dysentery in Sichuan Province.Frontiers in public health · 2025Article
- Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.Annals of surgical oncology · 2024Observational
- Predicting inferior vena cava filter complications using machine learning.Journal of vascular surgery. Venous and lymphatic disorders · 2024Article
- Comparison of Predictive Factors of Flu Vaccine Uptake Pre- and Post-COVID-19 Using the NIS-Teen Survey.Vaccines · 2024Article
- Article
- Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.Journal of the American Heart Association · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe 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.
designTime-series study.
settingThe USA was the setting for this study.
main outcome measuresThree 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.
resultsIn 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.
conclusionsThe XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.
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