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

Authors

  • Ahmad Aldousari KU

DOI:

https://doi.org/10.48129/kjs.splcov.10273

Keywords:

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

Abstract

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)

References

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Published

06-12-2021

Issue

Section

Statistics