Dynamic analysis of a SEAIRS model on co-evolution of human behavior changes in COVID-19 epidemic

Considering that people usually take non-drug protective measures (making behavior changes), this paper proposes a new SEAIRS model by combining behavior changes during the spread of the COVID-19 epidemic. First, we prove the positivity and boundedness of the solution of the model. Furthermore, the existence and stability of the equilibrium points are discussed in three behavior scenarios $ (B_c = 0, \ B_c = 1, \ 0 < B_c < 1, $ where $ B_c $ represents the proportion of susceptible individuals ($ S $), exposed individuals ($ E $), and asymptomatic infected individuals ($ A $) who adopt preventive behavior changes). Finally, we illustrate the model's behaviour using COVID-19 data from Romania, and summarize some valuable results: (i) the spread of diseases can be effectively controlled by reducing the contact between susceptible, exposed, and infected individuals; (ii) increasing the proportion of behavioral changes can significantly reduce the number of infected individuals. The model suggests two main theoretical measures to promote behavior changes within the framework: one is to increase the return difference between the two behaviors by adjusting relevant parameters, and the other is to increase the value of the information function.