  This paper is concerned with nonlinear modeling and analysis of the COVID-19
pandemic currently ravaging the planet. There are two objectives: to arrive at
an appropriate model that captures the collected data faithfully, and to use
that as a basis to explore the nonlinear behavior. We use a nonlinear SEIR
(Susceptible, Exposed, Infectious & Removed) transmission model with added
behavioral and government policy dynamics. We develop a genetic algorithm
technique to identify key model parameters employing COVID19 data from South
Korea. Stability, bifurcations and dynamic behavior are analyzed. Parametric
analysis reveals conditions for sustained epidemic equilibria to occur. This
work points to the value of nonlinear dynamic analysis in pandemic modeling and
demonstrates the dramatic influence of social and government behavior on
disease dynamics.
