Dynamics and optimal control of an extended SIQR model with protected human class and public awareness

In this article, we have developed the SIQR type mathematical model including the protected human population and public awareness in the model for the dynamics of an epidemic outbreak. The “level of awareness”, due to awareness campaign, is taken as a separate model variable. Both local (information sharing from local area, relatives) and global awareness (information sharing from social media, Radio, TV, etc.) can increase the level of awareness. We have also included the impact of treatment for recovery from the infection. Also, we have assumed infection transmission as a decreasing function of media awareness. The existence of equilibria of the model and their stability nature have been studied with qualitative theory. The disease-free equilibrium is stable when R0<1 and unstable when R0<1. A unique endemic equilibrium exists when R0>1, and it shows a Hopf bifurcation if the infection rate crosses its critical value. The unstable endemic system becomes stable when the global awareness rate is increased. To obtain crucial insights into disease management strategies, sensitivity analysis is performed to examine the link between model parameters and the basic reproduction number R0. Finally, we formulate an optimal control problem including three control parameters and solved using Pontryagin’s maximum principle. Numerical simulations are executed on the basis of analytical results. The regions of stability of the disease-free equilibrium are identified in different parameter planes. We have determined the optimal profiles of the three control functions to make the disease management process economically viable. This study concluded that the transmission dynamics of the pandemic depend on the rate of infection, the rate of global awareness, and the rate of awareness-based treatments. The proposed awareness-induced mathematical model that includes an optimal control approach is applicable to cost-effectively manage an epidemic outbreak.