Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action

TL;DR

This paper develops a nonlinear SEIR model incorporating behavioral and policy factors to analyze COVID-19 dynamics, revealing how social and government actions significantly influence epidemic outcomes.

Contribution

It introduces a novel nonlinear model with behavioral and policy components and employs genetic algorithms for parameter identification using real data.

Findings

Identification of conditions for sustained epidemic equilibria.

Analysis of bifurcations and dynamic behaviors.

Demonstration of social and government influence on disease spread.

Abstract

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.