Abstract
COVID-19 pandemic has affected more than 144 million people and spread to over 200 countries. The prediction of COVID-19 behaviour and trend is crucial to prevent its spreading. Kingdom of Saudi Arabia (KSA) is Asia's fifth largest country, and it hosts the two holiest cities of the Islamic world. KSA hosts millions of pilgrims every year, and it is of great importance to predict the COVID-19 spread to organize these religious activities and bring life to normality in KSA. This study proposes four tree-based ensemble methods to predict the COVID-19 daily new cases in KSA. Tree-based ensemble methods are suggested to reduce the variance and/or bias of inconsistent models. The four models utilized in the study are Gradient Tree Boosting (GB), Random Forest (RF), Extreme Gra-dient Boosting (XGBoost) and Voting Regressor (VR). The study is conducted using "Our Data in World" (OWID) COVID-19 dataset from the first confirmed case in KSA, i.e., 2nd March 2020 to 14th April 2021. The results suggest that the tree-based ensemble models provide a good prediction of daily COVID-19 new cases and can follow the trend of COVID-19. Among the models, XGBoost and VR performed better than the other three models with the best evaluation metric scores (MAE:4.41, RMSE:7.11, MAPE:0.95%). The significant prediction power of the tree-based ensemble methods, especially XGBoost can provide the platform for policymakers to put strategic plans for the closure periods of the edu-cational institutions and organize Hajj and Umrah.