Development of novel hybrid models for the prediction of COVID-19 in Kuwait. (Special issue on COVID.)
Kuwait Journal of Science
; (on)2021.
Article
in English
| GIM | ID: covidwho-2312349
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.
coronavirus disease 2019; disease models; human diseases; lungs; pandemics; prediction; respiratory diseases; viral diseases; Severe acute respiratory syndrome coronavirus 2; Kuwait; high income countries; Persian Gulf States; Middle East; West Asia; Asia; very high Human Development Index countries; Severe acute respiratory syndrome-related coronavirus; Betacoronavirus; Coronavirinae; Coronaviridae; Nidovirales; positive-sense ssRNA Viruses; ssRNA Viruses; RNA Viruses; viruses; lung diseases; SARS-CoV-2; viral infections
Full text:
Available
Collection:
Databases of international organizations
Database:
GIM
Type of study:
Prognostic study
Language:
English
Journal:
Kuwait Journal of Science
Year:
2021
Document Type:
Article
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