Optimal Control of Infectious Diseases Using the Artificial Neural Networks

This paper presents a novel approach using artificial neural networks to solve the SEIR (Susceptible‎, ‎Exposed‎, ‎Infected‎, ‎and Recovered) model of infectious diseases based on dynamical systems‎. ‎Optimal control techniques are employed to determine a vaccination schedule for a standard SEIR epidemic model‎. ‎The multilayer perceptron is utilized to approximate the state and co-state functions of the SEIR model and to solve the optimal control problem by utilizing a nonlinear programming approach‎. By constructing a loss function and using Pontryagin's Minimum Principle (PMP) for the SEIR model, a minimization problem is defined, ‎a minimization problem is defined‎, ‎and the approximate solution of the Hamiltonian system is computed‎. ‎This method is compared with the fourth-order Runge-Kutta method. The proposed approach's effectiveness is demonstrated through illustrative examples.