Comprehensive Review on Statistical Modeling Approach to Predict the COVID-19 Transmission
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Atlantis Press International BV
Reference45 articles.
1. Peng, Y., Nagata, M.H.: An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. Chaos, Solitons & Fractals. 139, 110055 (2020). https://doi.org/10.1016/j.chaos.2020.110055.
2. Kavadi, D.P., Patan, R., Ramachandran, M., Gandomi, A.H.: Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19. Chaos, Solitons & Fractals. 139, 110056 (2020). https://doi.org/10.1016/j.chaos.2020.110056.
3. Alali, Y., Harrou, F., Sun, Y.: A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Sci Rep. 12, 2467 (2022). https://doi.org/10.1038/s41598-022-06218-3.
4. Zeroual, A., Harrou, F., Dairi, A., Sun, Y.: Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study. Chaos, Solitons & Fractals. 140, 110121 (2020). https://doi.org/10.1016/j.chaos.2020.110121.
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