It is increasingly recognized that understanding the complex interplay patterns between epidemic spreading and human behavioral is a key component of successful infection control efforts. In particular, individuals can obtain the information about epidemics and respond by altering their behaviors, which can affect the spreading dynamics as well. Besides, because the existence of herd-like behaviors, individuals are very easy to be influenced by the global awareness information. Here, in this paper, we propose a global awareness controlled spreading model (GACS) to explore the interplay between the coupled dynamical processes. Using the global microscopic Markov chain approach, we obtain the analytical results for the epidemic thresholds, which shows a high accuracy by comparison with lots of Monte Carlo simulations. Furthermore, considering other classical models used to describe the coupled dynamical processes, including the local awareness controlled contagion spreading (LACS) model, Susceptible–Infected–Susceptible–Unaware–Aware– Unaware (SIS–UAU) model and the single layer occasion, we make a detailed comparisons between the GACS with them. Although the comparisons and results depend on the parameters each model has, the GACS model always shows a strong restrain effects on epidemic spreading process. Our results give us a better understanding of the coupled dynamical processes and highlights the importance of considering the spreading of global awareness in the control of epidemics.

Introduction

The study of networks has experienced a burst of activity in the last two decades [1–۵]. In the field of physics, most approaches to these problems are related to the theory of phase transition [6], statistical physics [7] and critical phenomenon [8–۱۰]. Especially with the rapid development of Internet and social media, the study of diffusion processes has attracted more and more interests [11–۱۳]. The use of network theory in epidemiological models provides a way to incorporate the individual-level heterogeneity necessary for the mechanistic understanding of the spread of infectious disease [14]. And as a result, many interesting models have been proposed to help us to gain the details of these processes, such as the classical susceptible–infected–susceptible model (SIS) [15], susceptible–infected–recovery model (SIR) [16] and so on [17,18]. Different models correspond to diverse realistic scenarios and they may focus on various factors which can affect epidemic spreading, e.g., the structure of networks [19], the frequency of contacts among individuals [20], the heterogeneity of participants [21], the transmission capacity of community structure [22,23], etc.