Fractional-order SEIQRDP model for simulating the dynamics of COVID-19 epidemic

Abstract

The novel coronavirus disease (COVID-19) is known as the causative virus of outbreak pneumonia initially recognized in the mainland of China, late December 2019. COVID-19 reaches out to many countries in the world, and the number of daily cases continues to increase rapidly. In order to simulate, track, and forecast the trend of the virus spread, several mathematical and statistical models have been developed. Susceptible-Exposed-Infected-Quarantined-Recovered-Death-Insusceptible (SEIQRDP) model is one of the most promising dynamic systems that has been proposed for estimating the transmissibility of the COVID-19. In the present study, we propose a Fractional-order SEIQRDP model to analyze the COVID-19 epidemic. The Fractional-order paradigm offers a flexible, appropriate, and reliable framework for pandemic growth characterization. In fact, fractional-order operator is not local and consider the memory of the variables. Hence, it takes into account the sub-diffusion process of confirmed and recovered cases growth. The results of the validation of the model using real COVID-19 data are presented, and the pertinence of the proposed model to analyze, understand, and predict the epidemic is discussed.