This paper proposes an impulsive control scheme for a complex network that helps reduce the spread of two epidemic diseases: influenza type A and COVID-19. Both are respiratory infections; thus, they have a similar form of transmission, and it is possible to use the same control scheme in both study cases. The objective of this work is to use neural impulsive inverse optimal pinning control for complex networks to reduce the effects of propagation. The dynamic model is considered unknown, for which we design a neural identifier that, through training using the extended Kalman filter algorithm, provides the appropriate nonlinear model for this complex network. The dynamics of the network nodes are represented by the Susceptible-Infected-Removed (SIR) compartmental model in their discrete form. The results of the simulations are presented and addressed, applying the same control scheme but with different parameter values for each case study.

This work introduces an impulsive neural control algorithm designed to mitigate the spread of epidemic diseases. The objective of this paper is the development of a vaccination strategy based on a PIN-type impulsive controller based on an online-trained neural identifier to control the spread of infectious diseases under a complex network approach with time-varying connections where each node represents a population of individuals whose dynamics are defined by the MSEIR epidemiological model. Considering an unknown model of the system, a neural identifier is designed that provides a nonlinear model for the complex network trained through an extended Kalman filter algorithm. Simulation results are presented by applying the proposed control scheme for a complex network parameterized as infectious diseases.