Machine Learning–Based Approaches for COVID-19 Transmission Modeling and Prediction: A Comprehensive Investigation

Yitong Zheng

doi:10.54097/nejse691

Authors

Yitong Zheng

DOI:

https://doi.org/10.54097/nejse691

Keywords:

Machine-learning, Covid-19, deep learning models.

Abstract

Due to several outbreaks of large infectious diseases, it is crucial to make accurate predictions of dynamics of the pandemics. Among all the tools, machine learning proves to be an efficient way for predicting infectious diseases, such as Covid-19. This paper reviews the use of machine learning algorithms to forecast the transmission dynamics of COVID-19. It outlines the fundamental workflow of both traditional methods (Random Forest, Decision Tree and Linear Regression) and deep learning models (Bi-Gru, RNN, DNN). While these data-driven approaches demonstrate significant capability in capturing complex patterns and making predictions, there are still reliability issues of these methods in real-world health decision-making, such as interpretability, applicability and error accumulation. To address the limitations, the paper proposes three methods, including expert system, transfer learning and detailed data cleansing and collection process. The conclusion advocates for hybrid frameworks that merge professional epidemiological knowledge with machine learning to build more robust and reliable tools for public health decision-making.

References

[1] Chen, W., Luo, H., Li, J., & Chi, J. 2024. Long-term trend prediction of pandemic combining the compartmental and deep learning models. Scientific Reports, 14 (1), 21068.

[2] Jung, S. 2025. Can the number of confirmed COVID-19 cases be predicted more accurately by including lifestyle data? An exploratory study for data-driven prediction of COVID-19 cases in metropolitan cities using deep learning models. Digital Health, 11, 20552076251314528.

[3] Kaur, G., et al. 2023. Forecasting prediction of COVID-19 outbreak using linear regression. In I. J. Jacob, S. Kolandapalayam Shanmugam, & I. Izonin (Eds.), Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore.

[4] Lipton, Z. C. 2015. A critical review of recurrent neural networks for sequence learning. arXiv (abs/1506.00019).

[5] Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E., & Getz, W. M. 2005. Superspreading and the effect of individual variation on disease emergence. Nature, 438 (7066), 355 – 359.

[6] Mohammadi-Pirouz, H., et al. 2024. Development of decision tree classification algorithms in predicting mortality of COVID-19 patients. International Journal of Emergency Medicine, 17 (1), 126.

[7] Qian, Y., et al. 2025. Physics-informed deep learning for infectious disease forecasting.

[8] Riou, J., & Althaus, C. L. 2020. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance, 25 (4), 2000058.

[9] Song, Y., & Lu, Y. 2015. Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27 (2), 130 – 135.

[10] Vali, A., Comai, S., & Matteucci, M. 2024. An automated machine learning framework for adaptive and optimized hyperspectral-based land cover and land-use segmentation. Remote Sensing, 16 (14), 2561.

[11] Weiss, H. M. 2013. The SIR model and the foundations of public health.

[12] Zhou, S. 2024. Using SARIMA method and random forest to predict the COVID-19 infection cases. Advances in Economics, Management and Political Sciences, 140 (1), 1 – 13.

[13] Zhang, L., Shen, L., Ding, L., Tao, D., & Duan, L. Y. 2022. Fine-tuning global model via data-free knowledge distillation for non-iid federated learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10174 - 10183).