Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action
- PDF / 961,161 Bytes
- 15 Pages / 547.087 x 737.008 pts Page_size
- 67 Downloads / 160 Views
(0123456789().,-volV) (0123456789().,-volV)
ORIGINAL PAPER
Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action C. A. K. Kwuimy . Foad Nazari . Xun Jiao . Pejman Rohani . C. Nataraj
Received: 12 May 2020 / Accepted: 8 July 2020 Ó Springer Nature B.V. 2020
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 susceptible, exposed, infectious and removed transmission model with added behavioral and government policy dynamics. We develop a genetic algorithm technique to identify key model parameters employing COVID-19 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 C. A. K. Kwuimy Department of Engineering Education, University of Cincinnati, Cincinnati, OH, USA e-mail: [email protected] F. Nazari X. Jiao C. Nataraj (&) Villanova Center for Analytics of Dynamic Systems (VCADS), Villanova University, Villanova, PA, USA e-mail: [email protected] F. Nazari e-mail: [email protected] X. Jiao e-mail: [email protected] P. Rohani Odum School of Ecology, The University of Georgia, Athens, GA, USA
nonlinear dynamic analysis in pandemic modeling and demonstrates the dramatic influence of social and government behavior on disease dynamics. Keywords SEIR model Epidemiology COVID19 Nonlinear dynamics
1 Introduction Coronavirus disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) that was first identified in China in early December 2019. It has since become a global pandemic devastating the health, economy and lives of billions of people all over the world and has brought into sharp focus the need for accurate modeling of infectious diseases. The global government policies are in fact largely being driven by statistical analyses loosely based on nonlinear mathematical models that underlie epidemiology. As we write this paper, there is also a rising controversy about the predictive power of these models. The crux of the matter is that there is a trade-off between economic disruptions and deaths. If the model predictions are incorrect in terms of overprediction, we may be creating mass unemployment and hurting billions of lives by causing economic deprivation. On the other hand, if the model
123
C. A. K. Kwuimy et al.
predictions are wrong through underprediction, then too many unnecessary deaths would occur. This quandary that most political leaders are finding themselves in points to the need for high accuracy in the models. Mathematical modeling in epidemiology has a long history dating back to early models by Bernoulli in the eighteenth century [1, 2], although mo
Data Loading...
