Fractional-Order SIR Epidemic Model for Transmission Prediction of COVID-19 Disease

Fractional-Order SIR Epidemic Model for Transmission Prediction of COVID-19 Disease

Kamil Kozioł, Rafał Stanisławski, Grzegorz Bialic

In this paper, the fractional-order generalization of the susceptible-infected-recovered (SIR) epidemic model for predicting the spread of the COVID-19 disease is presented. The time-domain model implementation is based on the fixed-step method using the nabla fractional-order difference defined by Grünwald-Letnikov formula. We study the influence of fractional order values on the dynamic properties of the proposed fractional-order SIR model. In modeling the COVID-19 transmission, the model’s parameters are estimated while using the genetic algorithm. The model prediction results for the spread of COVID-19 in Italy and Spain confirm the usefulness of the introduced methodology.

2020

covid-19, SIR epidemic model, Fractional order systems

10.3390/app10238316

Epidemiology and Health

Korean Society of Epidemiology

Biology (General), Chemistry, Engineering (General). Civil engineering (General), Technology, Physics