学术文献库

During decades, mathematical models have been used to predict the behavior of physical and biologic systems, and to define strategies aiming the minimization of the effects regarding different types of diseases. In the present days, the development of mathematical models to simulate the dynamic behavior of novel coronavirus disease (COVID-19) is considered an important theme due to the quantity of infected people worldwide. In this work, the aim is to determine an optimal control strategy for vaccine administration in COVID-19 pandemic treatment considering real data from China. For this purpose, an inverse problem is formulated and solved in order to determine the parameters of the compartmental SIR (Susceptible-Infectious-Recovered) model. To solve such inverse problem, the Differential Evolution (DE) algorithm is employed. After this step, two optimal control problems (mono- and multi-objective) to determine the optimal strategy for vaccine administration in COVID-19 pandemic treatment are proposed. The first consists of minimizing the quantity of infected individuals during the treatment. The second considers minimizing together the quantity of infected individuals and the prescribed vaccine concentration during the treatment, i.e., a multi-objective optimal control problem. The solution of each optimal control problems is obtained using DE and Multi-Objective Differential Evolution (MODE) algorithms, respectively. The results regarding the proposed multi-objective optimal control problem provides a set of evidences from which an optimal strategy for vaccine administration can be chosen, according to a given criterion.