Θ-SEIHRD mathematical model of Covid19-stability analysis using fast-slow decomposition

Θ-SEIHRD mathematical model of Covid19-stability analysis using fast-slow decomposition

OPhir Nave, Israel Hartuv, Uziel Shemesh

In general, a mathematical model that contains many linear/nonlinear differential equations, describing a phenomenon, does not have an explicit hierarchy of system variables. That is, the identification of the fast variables and the slow variables of the system is not explicitly clear. The decomposition of a system into fast and slow subsystems is usually based on intuitive ideas and knowledge of the mathematical model being investigated. In this study, we apply the singular perturbed vector field (SPVF) method to the COVID-19 mathematical model of to expose the hierarchy of the model. This decomposition enables us to rewrite the model in new coordinates in the form of fast and slow subsystems and, hence, to investigate only the fast subsystem with different asymptotic methods. In addition, this decomposition enables us to investigate the stability analysis of the model, which is important in case of COVID-19. We found the stable equilibrium points of the mathematical model and compared the results of the model with those reported by the Chinese authorities and found a fit of approximately 96 percent.

2020

coronavirus, Mathematical modeling, covid-19, Stability Analysis, Singular perturbed system

10.7717/peerj.10019

Epidemiology and Health

Korean Society of Epidemiology

Medicine