Fractional-Order SIR Epidemic Model for Transmission Prediction of COVID-19 Disease
Kamil Kozioł, Rafał Stanisławski, Grzegorz Bialic
Abstract
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.
Topics & Concepts
Epidemic modelCoronavirus disease 2019 (COVID-19)GeneralizationApplied mathematicsMathematicsOrder (exchange)Transmission (telecommunications)Computer scienceMathematical optimizationDiseaseMathematical analysisInfectious disease (medical specialty)MedicinePopulationTelecommunicationsFinancePathologyEnvironmental healthEconomicsCOVID-19 epidemiological studiesFractional Differential Equations SolutionsSARS-CoV-2 and COVID-19 Research