Multistage variational iteration method for a SEIQR COVID-19 epidemic model with isolation class

Abstract

The SEIQR COVID-19 epidemic model is in the form of the system of first-order nonlinear differential equations. In this paper we propose multistage versions of the variational iteration method (VIM) to solve this COVID-19 epidemic model. The idea of multistage version is to divide the entire time domain into a finite number of subintervals and then implementing the VIM piecewisely on each subinterval. There are two kinds of multistage methods discussed in this paper, where the difference between the two methods lies in the number of restricted variations used in the correction functional. The multistage methods generally give more accurate solutions on longer time intervals than the classical versions. The multistage VIM with less number of restricted variations has the best performance among all types of variational iteration methods discussed in this paper. The accuracy of multistage VIM solution can be increased by using smaller size of subinterval or by implementing more iterations in each subinterval.