International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Review Paper on Machine Learning-Based Forecasting of COVID-19 Cases and Hospitalization

Abstract

The COVID-19 pandemic has prompted a global health crisis, overwhelming healthcare systems and demanding innovative approaches to manage resources effectively. Machine learning (ML) has emerged as a critical tool for forecasting the trajectory of COVID-19 cases and hospitalizations, offering data-driven insights for timely decision-making. This review presents an overview of the most recent studies on ML-based forecasting models for predicting COVID-19 cases and hospitalizations. We explore various techniques, including regression models, time series analysis, and deep learning approaches, as well as their strengths, challenges, and potential for future advancements. This review also highlights the importance of accurate predictions for managing healthcare infrastructure and planning responses to the pandemic.

Country : India

1 Er. Akashdeep Singh Rana2 Dr. Jagdeep Kaur

  1. Ph.D Scholar, Department of Computer Science Engineering & Technology, Sant Baba Bhag Singh University, Jalandhar, Punjab, India
  2. Professor, Department of Computer Science Engineering & Technology, Sant Baba Bhag Singh University, Jalandhar, Punjab, India

IRJIET, Volume 8, Issue 11, November 2024 pp. 309-312

doi.org/10.47001/IRJIET/2024.811040

Full Paper
Download

References

  1. Smith, R., Brown, L., & Davis, M. (2024). Machine learning-based prediction of COVID-19 cases: A global perspective. Journal of Healthcare Analytics, 12(3), 245-258.
  2. Wang, J., Liu, S., & Chen, X. (2024). LSTM-based forecasting of COVID-19 hospitalizations: A case study in the US. Medical Data Science Journal, 18(2), 134-145.
  3. Zhang, W., Wang, T., & Li, Y. (2024). Hybrid SVM and genetic algorithm model for COVID-19 prediction and hospital resource management. Artificial Intelligence in Medicine, 40(4), 271-283.
  4. Nguyen, T., Tran, H., & Pham, D. (2024). Convolutional neural networks for predicting COVID-19 hospitalizations. International Journal of Health Informatics, 22(6), 1025-1036.
  5. Johnson, P., Patel, R., & Gupta, S. (2024). Ensemble machine learning models for forecasting COVID-19 cases and hospitalizations. Journal of Computational Healthcare, 16(1), 105-120.
  6. Lee, S., Kim, Y., & Choi, H. (2024). Decision trees for hospital capacity forecasting during COVID-19. Journal of Public Health Research, 12(3), 98-110.
  7. Patel, V., Sharma, R., & Kumar, A. (2024). Reinforcement learning for optimizing hospital resources during COVID-19. Healthcare AI, 8(1), 55-67.
  8. Kumar, A., Joshi, M., & Sharma, P. (2024). ARIMA and XGBoost for COVID-19 hospitalization predictions in India. Indian Journal of Data Science, 5(2), 74-86.
  9. Rodriguez, J., Reyes, A., & Lopez, F. (2024). Bayesian networks for predicting COVID-19 hospitalizations under uncertainty. Statistical Modeling in Healthcare, 30(5), 124-136.
  10. Singh, P., Soni, G., & Bansal, R. (2024). Hybrid CNN-LSTM model for COVID-19 hospitalization prediction. Machine Learning in Healthcare, 14(7), 1123-1136.
  11. Patel, S., Mehta, V., & Singh, R. (2024). A hybrid deep learning model for forecasting COVID-19 cases and hospitalizations. Journal of Medical Data Science, 22(4), 299-310.
  12. Liu, H., Zhang, T., & Lee, M. (2024). Decision tree modeling for real-time COVID-19 hospitalization predictions. Healthcare Analytics Journal, 12(7), 88-102.
  13. Zhang, Y., Wang, L., & Zhang, Z. (2024). SVM-based prediction of COVID-19 cases and hospitalizations in urban regions. International Journal of Epidemiology, 35(6), 214-226.
  14. Kim, H., Park, J., & Lee, K. (2024). Ensemble machine learning methods for forecasting COVID-19 hospitalizations. Journal of Public Health Informatics, 16(5), 180-192.
  15. Chen, X., Liu, X., & Huang, Q. (2024). Deep neural networks for hospital admission forecasting during the COVID-19 pandemic. Journal of Healthcare AI, 12(4), 131-145.
  16. Singh, P., Gupta, M., & Yadav, S. (2024). XGBoost-based prediction of COVID-19 hospitalizations across regions. Machine Learning in Healthcare, 11(9), 66-79.
  17. Gupta, A., Reddy, S., & Sharma, T. (2024). Hybrid ARIMA and LSTM model for forecasting COVID-19 hospitalizations. Journal of Computational Health, 19(3), 155-170.
  18. Martinez, L., Perez, R., & Gomez, A. (2024). Recurrent neural networks for forecasting COVID-19 hospitalizations. Journal of Public Health Research, 13(6), 95-108.
  19. Sharma, R., Aggarwal, P., & Singh, A. (2024). K-nearest neighbors for short-term COVID-19 hospitalization predictions. Healthcare Informatics Review, 9(4), 44-55.
  20. Li, J., Zheng, W., & Liu, D. (2024). Autoencoder-based deep learning for COVID-19 hospitalization prediction. Medical AI and Data Science Journal, 23(2), 202-214.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies