Effects of individual heterogeneity and multi-type information on the coupled awareness-epidemic dynamics in multiplex networks

Abstract

Awareness of epidemics can influence people’s behavior and further trigger changes in epidemic spreading. Previous studies concentrating on the coupled awareness-epidemic dynamics usually ignore the multi-type information and the heterogeneity of individuals. However, the real-world cases can be more complicated, and the interaction between information diffusion and epidemic spreading needs further study. In this article, we propose an individual-based epidemics and multi-type information spreading (IEMIS) model on two-layered multiplex networks considering positive and negative preventive information and two types of heterogeneity: 1) heterogeneity of aware individual’s state which leads to differences in aware transmission capacity and 2) heterogeneity of individual’s node degree which affects the epidemic infection rate. Based on Micro-Markov Chain approach (MMCA), we derive the theoretical epidemic threshold for the proposed model and validate the results by those obtained with Monto Carlo (MC) simulations. Through extensive simulations, we demonstrate that for epidemics with low infectivity, promoting the diffusion of positive preventive information, enhancing the importance ratio of neighbors who are aware of positive information, and increasing social distance among individuals can effectively suppress epidemic spreading. However, for highly infectious diseases, the influence of these factors becomes limited.