Fractional-order SEIQRDP Model for Simulating the Dynamics of COVID-19 Epidemic
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Estimation, Modeling and ANalysis Group
Preprint Posting Date2020-05-04
Permanent link to this recordhttp://hdl.handle.net/10754/662790
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AbstractGoal: 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.
CitationBahloul, 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.2020.3019758
SponsorsResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST)
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