Fractional-order SEIQRDP Model for Simulating the Dynamics of COVID-19 Epidemic
Type
ArticleKAUST Department
Computational Bioscience Research Center (CBRC)Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering
Electrical Engineering Program
Estimation, Modeling and ANalysis Group
Date
2020Preprint Posting Date
2020-05-04Submitted Date
2020-05-04Permanent link to this record
http://hdl.handle.net/10754/662790
Metadata
Show full item recordAbstract
Goal: Coronavirus disease (COVID-19) is a contagious disease caused by a newly discovered coronavirus, initially identified in the mainland of China, late December 2019. COVID-19 has been confirmed as a higher infectious disease that can spread quickly in a community population depending on the number of susceptible and infected cases and also depending on their movement in the community. Since January 2020, COVID-19 has reached out to many countries worldwide, and the number of daily cases remains to increase rapidly. Method: Several mathematical and statistical models have been developed to understand, track, and forecast the trend of the virus spread. Susceptible-Exposed-Infected-Quarantined- Recovered-Death-Insusceptible (SEIQRDP) model is one of the most promising epidemiological models that has been suggested for estimating the transmissibility of the COVID-19. In the present study, we propose a fractional-order SEIQRDP model to analyze the COVID-19 pandemic. In the recent decade, it has proven that many aspects in many domains can be described very successfully using fractional order differential equations. Accordingly, the Fractional-order paradigm offers a flexible, appropriate, and reliable framework for pandemic growth characterization. In fact, due to its non-locality properties, a fractional-order operator takes into consideration the variables' memory effect, and hence, it takes into account the sub-diffusion process of confirmed and recovered cases. Results' The validation of the studied fractional-order model using real COVID-19 data for different cities in China, Italy, and France show the potential of the proposed paradigm in predicting and understanding the pandemic dynamic. Conclusions: Fractional-order epidemiological models might play an important role in understanding and predicting the spread of the COVID-19, also providing relevant guidelines for controlling the pandemic.Citation
Bahloul, M. A., Chahid, A., & Laleg-Kirati, T.-M. (2020). Fractional-Order SEIQRDP Model for Simulating the Dynamics of COVID-19 Epidemic. IEEE Open Journal of Engineering in Medicine and Biology, 1, 249–256. doi:10.1109/ojemb.2020.3019758Sponsors
Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST)Publisher
IEEEarXiv
2005.01820Additional Links
https://arxiv.org/pdf/2005.01820https://ieeexplore.ieee.org/document/9178435/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9178435
https://ieeexplore.ieee.org/ielx7/8782705/8819998/09178435.pdf
ae974a485f413a2113503eed53cd6c53
10.1109/OJEMB.2020.3019758
Scopus Count
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