(Received: 22-Dec.-2021, Revised: 27-Feb.-2022 , Accepted: 25 -Mar.-2022 )
The world is currently facing the coronavirus disease 2019 (COVID-19 pandemic). Forecasting the progression of that pandemic is integral to planning the necessary next steps by governments and organizations. Recent studies have examined the factors that may impact COVID-19 forecasting and others have built models for predicting the numbers of active cases, recovered cases and deaths. The aim of this study was to improve the forecasting predictions by developing an ensemble machine-learning model that can be utilized in addition to the Naïve Bayes classifier, which is one of the simplest and fastest probabilistic classifiers. The first ensemble model combined gradient boosting and random forest classifiers and the second combined support vector machine and random-forest classifiers. The numbers of confirmed, recovered and death cases will be predicted for a period of 10 days. The results will be compared to the findings of previous studies. The results showed that the ensemble algorithm that combined gradient boosting and random-forest classifiers achieved the best performance, with 99% accuracy in all cases.

[1] Centers for Disease Control and Prevention, "Severe Outcomes among Patients with Coronavirus Disease 2019 (COVID-19) : United States," MMWR Morb. Mortal. Wkly. Rep., vol. 69, no. 12, pp. 343-346, February 12–March 16, 2020.

[2] I. M. Hall et al., "Real-time Epidemic Forecasting for Pandemic Influenza," Epidemiology and Infection, vol. 135, no. 3, pp. 372-385, 2007.

[3] K. Sarkar, S. Khajanchi and J. Nieto, "Modeling and Forecasting the COVID-19 Pandemic in India," Chaos, Solitons & Fractals, vol. 139, DOI: 10.1016/j.chaos.2020.110049, 2020.

[4] F. Rustam, A. Reshi, A. Mehmood, S. Ullah, B. Won On et al., "COVID-19 Future Forecasting Using Supervized Machine Learning Models," IEEE Access, vol. 8, pp. 101489–101499, 2020.

[5] Y. Mohamadou, A. Halidou and P. T. Kapen, "A Review of Mathematical Modeling, Artificial Intelligence and Datasets Used in the Study, Prediction and Management of COVID-19," Applied Intelligence, vol. 50, pp. 3913–3925, 2020.

[6] M. Otooma, N. Otoumb, M. A. Alzubaidi, Y. Etoom and R. Banihani, "An IoT-based Framework for Early Identification and Monitoring of COVID-19 Cases," Biomedical Signal Processing and Control, vol. 62, DOI : 10.1016/j.bspc.2020.102149, 2020.

[7] M. Nemati, J. Ansary and N. Nemati, "Machine-learning Approaches in COVID-19 Survival Analysis and Discharge-time Likelihood Prediction Using Clinical Data," Patterns, vol. 1, no. 5, 2020.

[8] C. Iwendi, A. K. Bashir, A. Peshkar et al., "COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm," Frontiers in Public Health, vol. 357, DOI : 10.3389/fpubh.2020.00357, 2020.

[9] A. Farid, G. Selim and H. Khater, "Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)," International Journal of Scientific and Engineering Research, vol. 11, no. 3, DOI:10.14299/ijser.2020.03.02, 2020.

[10] A. A. Reshi et al., "An Efficient CNN Model for COVID-19 Disease Detection Based on X-ray Image Classification," Complexity, vol. 2021, DOI: 10.1155/2021/6621607, 2021.

[11] S. Dash et al., "Intelligent Computing on Time-series Data Analysis and Prediction of COVID-19 Pandemic," Pattern Recognition Letters, vol. 151, pp. 69-75, 2021.

[12] S. F. Ardabili et al., "COVID-19 Outbreak Prediction with Machine Learning," Algorithms, vol. 13, no. 10, DOI: 10.3390/a13100249, 2020.

[13] G. Pinter et al., "COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach," Mathematics, vol.8, no.6, DOI: 10.3390/math8060890, 2020.

[14] S. Ardabili et al., "Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer," Proc. of the 3rd IEEE International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE), DOI: 10.1109/CANDO-EPE51100.2020.9337757, Budapest, Hungary, 2020.

[15] F. Rustam et al., "A Performance Comparison of Supervized Machine Learning Models for COVID-19 Tweets Sentiment Analysis," Plos One, vol. 16, no. 2, DOI: 10.1371/journal.pone.0245909, 2021.

[16] H. Khaloufi et al., "Deep Learning Based Early Detection Framework for Preliminary Diagnosis of COVID-19 via Onboard Smartphone Sensors," Sensors, vol. 21, no. 20, DOI: 10.3390/s21206853, 2021.

[17] J. H. C. R. Center, Johns Hopkins Coronavirus Resource Center, [Online], Available:

[18] The COVID Tracking Project, [Online], Available:

[19] U. N. Dulhare, "Prediction System for Heart Disease Using Naive Bayes and Particle Swarm Optimization," Biomedical Research, vol. 29, pp. 2646–2649, DOI:10.4066/biomedicalresearch.29-18- 620, 2018.

[20] W. Zhang, C. Wu, H. Zhong, Y. Li and L. Wang, "Prediction of Undrained Shear Strength Using Extreme Gradient Boosting and Random Forest Based on Bayesian Optimization," Geoscience Frontiers, vol. 12, no. 1, pp. 469–477, 2021.

[21] V. Mohan, "Liver Disease Prediction Using SVM and Naive Bayes Algorithms," International Journal of Science, Engineering and Technology Research (IJSETR), vol. 4, no. 4, pp. 816–820, 2015.

[22] M. Loey, G. Manogaran, M. Taha and N. Khalifa, "A Hybrid Deep Transfer Learning Model with Machine Learning Methods for Face Mask Detection in the Era of the COVID-19 Pandemic," Measurement, vol. 167, DOI: 10.1016/j.measurement.2020.108288, 2020.

[23] J. L. Speiser, M. E. Miller, J. Tooze and E. Ip, "A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling," Expert Systems with Applications, vol. 134, pp. 93– 101, DOI: 10.1016/j.eswa.2019.05.028, 2019.

[24] J. Hopkins, Johns Hopkins University Data Repository, [Online], Available :

[25] N. Singhal et al., "Comparative Study of Machine Learning and Deep Learning Algorithm for Face Recognition," Jordanian Journal of Computers and Information Technology (JJCIT), vol. 7, no. 3, pp. 313-325, Sep. 2021.