On the fractional SIRD mathematical model and control for the transmission of COVID-19: The first and the second waves of the disease in Iran and Japan.

In this paper, a fractional-order SIRD mathematical model is presented with Caputo derivative for the transmission of COVID-19 between humans. We calculate the steady-states of the system and discuss their stability. We also discuss the existence and uniqueness of a non-negative solution for the system under study. Additionally, we obtain an approximate response by implementing the fractional Euler method. Next, we investigate the first and the second waves of the disease in Iran and Japan; then we give a prediction concerning the second wave of the disease. We display the numerical simulations for different derivative orders in order to evaluate the efficacy of the fractional concept on the system behaviors. We also calculate the optimal control of the system and display its numerical simulations.

A new and efficient numerical method for the fractional modeling and optimal control of diabetes and tuberculosis co-existence.

The main objective of this research is to investigate a new fractional mathematical model involving a nonsingular derivative operator to discuss the clinical implications of diabetes and tuberculosis coexistence. The new model involves two distinct populations, diabetics and nondiabetics, while each of them consists of seven tuberculosis states: susceptible, fast and slow latent, actively tuberculosis infection, recovered, fast latent after reinfection, and drug-resistant. The fractional operator is also considered a recently introduced one with Mittag-Leffler nonsingular kernel. The basic properties of the new model including non-negative and bounded solution, invariant region, and equilibrium points are discussed thoroughly. To solve and simulate the proposed model, a new and efficient numerical method is established based on the product-integration rule. Numerical simulations are presented, and some discussions are given from the mathematical and biological viewpoints. Next, an optimal control problem is defined for the new model by introducing four control variables reducing the number of infected individuals. For the control problem, the necessary and sufficient conditions are derived and numerical simulations are given to verify the theoretical analysis.