Identification of COVID–19 dynamics and economic impact for a fractional SEIR model.

In the context of the COVID–19, a great deal of attention is attracted in studying the infectious disease from both academics and practitioners. One of the most powerful tools for simulating, analyzing and predicting the future epidemic development is the mathematical modelling. We adopted a fractional-order SEIR compartment model to investigate the disease underlying dynamics. The major aim of the present study is to propose an efficient algorithm to solve the coefficient identification inverse problem. The latter is formulated as a minimization problem for a quadratic cost functional, which is solved by a numerical optimization methods. The basic reproduction number and the economic impact of the epidemic are also discussed. Computational experiments with real-world data are provided to demonstrate the suggested algorithm properties. The paper is concluded with final remarks concerning the applicability of the approach and possible further research. [ABSTRACT FROM AUTHOR]