Development of novel hybrid models for the prediction of Covid-19 in Kuwait
DOI:
https://doi.org/10.48129/kjs.splcov.10273Keywords:
Kuwait, Covid-19, Empirical Mode Decomposition, Ensemble Empirical Mode Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Hybrid modelAbstract
The first case of coronavirus 2019 (Covid-19) in Kuwait was reported on February 24, 2020, and then day by day cases of Covid-19 keep on increasing. The decision of the government about the standard citizens to repatriate them back to Kuwait from different epicenters of Covid-19 has created a big challenge. There is a need to estimate a prediction model for the estimation of this epidemic size. The main objective of the current study is to find an efficient way of prediction of this epidemic situation for coronavirus by using real-time data from 24th February to 12th June 2020. By considering the uncertainty in the current situation and non-stationary real-time data of corona cases, we consider a novel strategy for prediction purposes. By using a multilayer model with different decomposition techniques, we developed a novel hybrid model for decomposition and prediction of corona cases in Kuwait. A Hybrid methodology based on denoising, decomposition, prediction, and ensemble rules are applied to the confirmed corona cases in Kuwait. To evaluate the performance of the novel hybrid model in comparison with other existing models, we use mean relative error, mean absolute error, and mean square error. It is concluded that the proposed framework for the prediction of conformed corona cases indicated better performance as compared to other existing methods.References
References
Almeshal, A. M., Almazrouee, A. I., Alenizi, M. R., & Alhajeri, S. N. (2020). Forecasting the spread of COVID-19 in kuwait using compartmental and logistic regression models. Applied Sciences. https://doi.org/10.3390/APP10103402
Boccaletti, S., Ditto, W., Mindlin, G., & Atangana, A. (2020). Modeling and forecasting of epidemic spreading: The case of Covid-19 and beyond. In Chaos, Solitons and Fractals. https://doi.org/10.1016/j.chaos.2020.109794
Chong, K. L., Lai, S. H., & El-Shafie, A. (2019). Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment. Water Resources Management. https://doi.org/10.1007/s11269-019-02226-7
Di, C., Yang, X., & Wang, X. (2014). A four-stage hybrid model for hydrological time series forecasting. PLoS ONE. https://doi.org/10.1371/journal.pone.0104663
Dybała, J., & Zimroz, R. (2014). Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal. Applied Acoustics. https://doi.org/10.1016/j.apacoust.2013.09.001
Husin, N. A., Mustapha, N., Sulaiman, M. N., & Yaakob, R. (2012). A hybrid model using genetic algorithm and neural network for predicting dengue outbreak. Conference on Data Mining and Optimization. https://doi.org/10.1109/DMO.2012.6329793
Islam, Z. (2011). Literature review on physically based hydrological modeling. In PhD Thesis. https://doi.org/10.13140/2.1.4544.5924
Jaitly, N., & Hinton, G. (2011). Learning a better representation of speech soundwaves using restricted boltzmann machines. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. https://doi.org/10.1109/ICASSP.2011.5947700
Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2015.12.003
Koczkodaj, W. W., Mansournia, M. A., Pedrycz, W., Wolny-Dominiak, A., Zabrodskii, P. F., Strzałka, D., Armstrong, T., Zolfaghari, A. H., Dębski, M., & Mazurek, J. (2020). 1,000,000 cases of COVID-19 outside of China: The date predicted by a simple heuristic. Global Epidemiology. https://doi.org/10.1016/j.gloepi.2020.100023
Liu, H., Tian, H. Q., Chen, C., & Li, Y. fei. (2010). A hybrid statistical method to predict wind speed and wind power. Renewable Energy. https://doi.org/10.1016/j.renene.2009.12.011
Nazir, H. M., Hussain, I., Faisal, M., Shoukry, A. M., Gani, S., & Ahmad, I. (2019). Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis. Complexity. https://doi.org/10.1155/2019/2782715
Peng, T., Zhou, J., Zhang, C., & Fu, W. (2017). Streamflow forecasting using empirical wavelet transform and artificial neural networks. Water (Switzerland). https://doi.org/10.3390/w9060406
Santhosh, M., Venkaiah, C., & Vinod Kumar, D. M. (2018). Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2018.04.099
Srinivasan, D. (2008). Energy demand prediction using GMDH networks. Neurocomputing. https://doi.org/10.1016/j.neucom.2008.08.006
Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. https://doi.org/10.1109/ICASSP.2011.5947265
Wei, X., Lin, R., Liu, S., & Zhang, C. (2016). Improved EEMD denoising method based on singular value decomposition for the chaotic signal. Shock and Vibration. https://doi.org/10.1155/2016/7641027
Wu, Z., Huang, N. E., & Chen, X. (2009). The multi-dimensional ensemble empirical mode decomposition method. Advances in Adaptive Data Analysis. https://doi.org/10.1142/S1793536909000187
Yoneyama, T., Das, S., & Krishnamoorthy, M. (2012). A hybrid model for disease spread and an application to the SARS pandemic. JASSS. https://doi.org/10.18564/jasss.1782
Zhang, J., Jiang, R., Li, B., & Xu, N. (2019). An automatic recognition method of microseismic signals based on EEMD-SVD and ELM. Computers and Geosciences. https://doi.org/10.1016/j.cageo.2019.104318
