Application of Pontryagin’s Minimum Principle in the ArtificialNeural Network to Reduce the COVID‑19 Pandemic Effects

Introduction: in this study, we analyze the optimal intervention strategies that lead toreducing the effects of the covid-19 pandemic by artificial neural networks (anns). ouraim is to investigate the effects of optimal control strategies, such as the implementation ofgovernment intervention, testing, and vaccination policies during outbreaks.methods: we utilized a controlled sidarev model to study the progression of the covid-19pandemic. using pontryagin’s minimum principle (pmp) for the sidarev model, wedefined an unconstrained minimization problem. applying the hamiltonian conditions, weapproximated the obtained ordinary differential equations (ode) using anns. we utilizedthe multilayer perceptron (mlp) to obtain the approximate solution of the states and costatesfunctions.results: we observed the effects of optimal control strategies, and to show the efficiency ofthe proposed method, we compared it with the runge-kutta method through some examples.conclusion: using a mathematical model that simulates the behavior of the covid-19disease, we can examine the effects of controllers such as government interventions, tests andvaccinations with the neural network method. the results show that this method is useful insolving the problem of optimal control of infectious diseases.