Abstract
The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people’s reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explore the interactions between disease spreading and population response in terms of information diffusion and individuals’ alertness. We model the system by mapping multiplex networks into two-layer networks and incorporating individuals’ risk awareness, on the assumption that their response to the disease spreading depends on the size of the community they belong to. By comparing the final incidence of diseases in multiplex networks, we find that there is considerable mitigation of diseases spreading for full phase of spreading speed when individuals’ protection responses are introduced. Interestingly, the degree of community overlap between the two layers is found to be critical factor that affects the final incidence. We also analyze the consequences of the epidemic incidence in communities with different sizes and the impacts of community overlap between two layers. Specifically, as the diseases information makes individuals alert and take measures to prevent the diseases, the effective protection is more striking in small community. These phenomena can be explained by the multiplexity of the networked system and the competition between two spreading processes.
Highlights
Diseases spreading in a population takes place via the interactions of infected individuals with others
To better understand the effects of information spreading and how the incidence of the epidemics is affected by the community structure of two layers, we investigate the effects of several key factors of the model: infection rate, activation rate, the degree of neglect about the epidemic and other community features like community number and the overlap of communities of two layers
The networks used in the experiments are given as following: Both epidemic spreading layer and communication layer of multiplex networks are constructed by the “benchmark” algorithm proposed by Lancichinetti, Fortunato, and Radicchi (LFR) [32], with which the networks with community structures and power-law distribution of community size can be generated
Summary
Diseases spreading in a population takes place via the interactions of infected individuals with others. Different from the previous works on modeling epidemic-like spreading processes with multiplex networks, here we focus on the roles played by community structure in affecting disease spreading, especially the effect of community size. Different from the existing control strategies that implement edges removal when individuals become infected and develop symptoms, we simulate the dynamic processes on multiplex networks and modify the epidemic transmission rate when the susceptible individuals got the disease information from the online social network.
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