Different strategies to confront maize streak disease based on fractional optimal control formulation

Abstract

In this paper, we propose a general formulation for the transmission dynamics of maize streak virus (MSV) pathogen interaction with a pest invasion in the maize plant. The mathematical formalism for this model is dependent on Caputo fractional operator with modification of its parameters. In the considered model, the total population of maize plants is divided into two classes: susceptible, infected maize and the total population of leafhopper vector contains two compartments: susceptible, infected leafhopper vector, with a compartment for MSV pathogen. In addition, this fractional-order model (FOM) is involving the proportion of three controls u1,u2 and u3 which namely respectively prevention, quarantine and chemical control. We present the positivity and boundedness of the projected solutions to assure the feasibility of solutions of this FOM. The control reproduction number (Rc) is derived by next generation matrix (NGM) method and showed graphically the effect of the controls for each proposed strategy on the behavior of Rc. The local stability analysis for all possible equilibrium points (EPs) has been examined in detail. Moreover, the fractional optimal control problem (FOCP) is characterized and fractional necessary optimality conditions (NOCs) are derived by using Pontryagin’s maximum principle (PMP). These NOCs are solved numerically, where the state and co-state equations based on the left Caputo fractional derivative (CFD). We offer four strategies to illustrate the effects of the proposed controls to investigate the preferable strategy for the elimination of maize streak disease (MSD), as each one of these strategies is able to alleviate this disease at a specific time. Finally, simulations are performed utilizing MATLAB with realistic ecological parameter values to demonstrate the obtained theoretical results. Comparative studies illustrated that infection of maize plants can be reduced through the proposed model, which has a significant impact on plant epidemiology.