Multicompartmental Mathematical Models of Infectious Dynamic Diseases With Time Fractional-Order Derivatives

Abstract

Nonlinear dynamic models with multiple compartments are characterized by subtle attributes like high dimensionality and heterogeneity, with fractional-order derivatives and constituting fractional calculus, which can provide a thorough comprehension, control and optimization of the related dynamics and structure. This requirement poses a formidable challenge, and thereby, has gained prominence in different fields where fractional derivatives and nonlinearities interact. Thus, fractional models have become relevant to address phenomena with memory effects, with fractional calculus providing amenities to deal with the time-dependent impacts observed. A novel infectious disease epidemic model with time fractional order and a Caputo fractional derivative type operator is discussed in the current study which is carried out for the considered epidemic model. Accordingly, a method for the semi-analytical solution of the epidemic model of a dynamic infectious disease with fractional order is employed in terms of the Caputo fractional derivative operator in this study. The existence and uniqueness of the solution is constructed with the aid of fixed point theory in particular. Furthermore, the Adams-Bashforth method, an extensively employed technique for the semi-analytical solution of these types of models. The simulation results for various initial data demonstrate that the solution of the considered model is stable and shows convergence toward a single point, and numerical simulations for different fractional orders lying between (0,1) and integer order have been obtained. On both initial approximations, the dynamical behavior of each compartment has shown stability as well as convergence. Consequently, the results obtained from our study based on experimental data can be stated to confirm the accurate total density and capacity for each compartment lying between two different integers considering dynamical processes and systems. © 2023 IEEE.