Fractional order mathematical modeling of COVID-19 transmission

Abstract

In this article, the mathematical model with different compartments for the transmission dynamics of coronavirus-19 disease (COVID-19) is presented under the fractional-order derivative. Some results regarding the existence of at least one solution through fixed point results are derived. Then for the concerned approximate solution, the modified Euler method for fractional-order differential equations (FODEs) is utilized. Initially, we simulate the results by using some available data for different fractional-order to show the appropriateness of the proposed method. Further, we compare our results with some reported real data against confirmed infected and death cases per day for the initial 67 days in Wuhan city.

An optimal control analysis of a COVID-19 model

“…[6] , [7] , [15] , [16] , [17] , [28] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44]…”

A mathematical model of Coronavirus Disease (COVID-19) containing asymptomatic and symptomatic classes

“…The main advantage of the operator is that it has a nonlocal and nonsingular kernel. More recently, new advances and studies in fractional differential equations has been published from a mathematical modeling point of view, see recent papers [52] , [19] , [27] , [30] , [46] , [15] , [8] , [28] , [44] , [29] , [7] , [4] , [12] , [11] .…”

A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19

“…However, the epidemic outbreaks regularly follow a non-Markovian process [7] , [10] , [11] , [12] . It means that the past states of the dynamical systems contribute to its current conditions, which can be claimed as “memory,” contrary to a Markovian process where the previous states do not affect the evolution [13] , [14] , [15] , [16] .…”