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Modeling and analysis of COVID-19 new deaths using tree-based ensemble

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posted on 09.09.2021, 12:46 by Ibrahim Abaker Targio Hashem, Raja Sher Afgun UsmaniRaja Sher Afgun Usmani, Asad Ali Shah, Abdulwahab Ali Almazroi, Muhammad Bilal
The COVID-19 pandemic has emerged as the world's most serious health crisis, affecting millions of people all over the world. The majority of nations have imposed nationwide curfews and reduced economic activity to combat the spread of this infectious disease. Governments are monitoring the situation and making critical decisions based on the daily number of new cases and deaths reported. Therefore, this study aims to predict the daily new deaths using four tree-based ensemble models i.e., Gradient Tree Boosting (GB), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Voting Regressor (VR) for the three most affected countries, which are the United States, Brazil, and India. The results showed that VR outperformed other models in predicting daily new deaths for all three countries. The predictions of daily new deaths made using VR for Brazil and India are very close to the actual new deaths, whereas the prediction of daily new deaths for the United States still needs to be improved.

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Email Address of Submitting Author

sher.afgun@uskt.edu.pk

ORCID of Submitting Author

https://orcid.org/0000-0003-2027-1425

Submitting Author's Institution

Department of Software Engineering, Faculty of Computing, and Information Technology, University of Sialkot

Submitting Author's Country

Pakistan

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