A new design and analysis of optimal control problems arising from COVID-19 outbreak

In this manuscript, we append the hospitalization, diagnosed and isolation compartments to the classic SEIR model to design a new COVID-19 epidemic model. We further subdivide the isolation compartment into asymptomatic infected and symptomatic infected compartments. For validity of the purposed model, we prove the existence of a unique solution and prove the positivity and boundedness of the solution. To study disease dynamics, we compute equilibrium points and the reproduction number R0$$ {R}_0 $$. We also investigate the local and global stabilities at both of the equilibrium points. Sensitivity analysis will be performed to observe the effect of transmission parameters on R0$$ {R}_0 $$. For optimal control analysis, we design two different optimal control problems by taking different optimal control approaches. Firstly, we add an isolation compartment in the newly designed model, and secondly, three parameters describing non-pharmaceutical behaviors such as educating people to take precautionary measures, providing intensive medical care with medication, and utilizing resources by government are added in the model. We set up optimality conditions by using Pontryagin's maximum principle and develop computing algorithms to solve the conditions numerically. At the end, numerical solutions will be displayed graphically with discussion.

Automated summary

In this manuscript, a new COVID-19 epidemic model is designed by incorporating the hospitalization, diagnosed, and isolation compartments into the classic SEIR model. The isolation compartment is further divided into asymptomatic infected and symptomatic infected compartments. The existence of a unique solution and the positivity and boundedness of the solution are proven to validate the proposed model. Equilibrium points and the reproduction number R0 are computed to study disease dynamics. Local and global stabilities at the equilibrium points are also investigated. Sensitivity analysis is conducted to observe the effect of transmission parameters on R0. Two different optimal control problems are designed for optimal control analysis. Pontryagin's maximum principle is used to establish optimality conditions, and computing algorithms are developed for numerical solution. Numerical solutions with discussion are displayed graphically.