A fractional-order model for the novel coronavirus (COVID-19) outbreak

• Published in: Nonlinear dynamics Vol. 101; no. 1; pp. 711 - 718
• Main Authors: Rajagopal, Karthikeyan, Hasanzadeh, Navid, Parastesh, Fatemeh, Hamarash, Ibrahim Ismael, Jafari, Sajad, Hussain, Iqtadar
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-07-2020; Springer Nature B.V
• Subjects: Automotive Engineering; Classical Mechanics; Control; Coronaviruses; COVID-19; Disease transmission; Dynamical Systems; Engineering; Epidemics; Mathematical models; Mechanical Engineering; Original Paper; Outbreaks; Parameter estimation; Vibration; Viral diseases; COVID-19; Fractional-order derivative; SEIRD model
• ISSN: 0924-090X; 1573-269X
• DOI: 10.1007/s11071-020-05757-6

The outbreak of the novel coronavirus (COVID-19), which was firstly reported in China, has affected many countries worldwide. To understand and predict the transmission dynamics of this disease, mathematical models can be very effective. It has been shown that the fractional order is related to the memory effects, which seems to be more effective for modeling the epidemic diseases. Motivated by this, in this paper, we propose fractional-order susceptible individuals, asymptomatic infected, symptomatic infected, recovered, and deceased (SEIRD) model for the spread of COVID-19. We consider both classical and fractional-order models and estimate the parameters by using the real data of Italy, reported by the World Health Organization. The results show that the fractional-order model has less root-mean-square error than the classical one. Finally, the prediction ability of both of the integer- and fractional-order models is evaluated by using a test data set. The results show that the fractional model provides a closer forecast to the real data.