Development of novel hybrid models for the prediction of Covid-19 in Kuwait


  • Ahmad Aldousari KU
  • Maria Qurban Dept. of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
  • Ijaz Hussain Dept. of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
  • Maha Al-Hajeri Dept. of Health Information and information management, College of Ailed Health, Kuwait University, Kuwait City, Kuwait.



Kuwait, Covid-19, Empirical Mode Decomposition, Ensemble Empirical Mode Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Hybrid model


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.  

Author Biography

Ahmad Aldousari, KU

Research Scientist, KISR (1990-2012)

Assistant Professor, Department of Geography, KU (2013-present)



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.

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.

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.

Di, C., Yang, X., & Wang, X. (2014). A four-stage hybrid model for hydrological time series forecasting. PLoS ONE.

Dybała, J., & Zimroz, R. (2014). Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal. Applied Acoustics.

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.

Islam, Z. (2011). Literature review on physically based hydrological modeling. In PhD Thesis.

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.

Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting.

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.

Liu, H., Tian, H. Q., Chen, C., & Li, Y. fei. (2010). A hybrid statistical method to predict wind speed and wind power. Renewable Energy.

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.

Peng, T., Zhou, J., Zhang, C., & Fu, W. (2017). Streamflow forecasting using empirical wavelet transform and artificial neural networks. Water (Switzerland).

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.

Srinivasan, D. (2008). Energy demand prediction using GMDH networks. Neurocomputing.

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.

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.

Wu, Z., Huang, N. E., & Chen, X. (2009). The multi-dimensional ensemble empirical mode decomposition method. Advances in Adaptive Data Analysis.

Yoneyama, T., Das, S., & Krishnamoorthy, M. (2012). A hybrid model for disease spread and an application to the SARS pandemic. JASSS.

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.