Networked SIRS Epidemic Model With Opinion Evolutions: Stubborn Community and Maximum Infection Time

Abstract

This paper is concerned with the co-evolution problem of epidemic and opinion over social networks. A networked SIRS epidemic model with opinion dynamics is proposed to analyze the impact of the community’s opinion on the epidemic spreading. By introducing stubborn communities, we give a sufficient condition to guarantee the epidemics converging the healthy state. Furthermore, the explicit relationship between the maximum infection time and the opinion based reproduction number is presented. Based on the Italy interactive network and the dataset collects tweets and accounts about vaccines on Twitter from March 1 to August 31, 2021, our proposed discrete-time epidemic-opinion model is employed to analyze the influence of vaccination on the epidemic spreading. It is shown that the vaccination can delay the peak of infections and reduce the overall infection rate in these communities.

Keywords

• Opinion dynamics
• epidemic spreading
• stubborn community
• maximum infection time
• social networks