The SIRS Model of Epidemic Spreading in Virtual Society

Abstract

The phenomenon of epidemic spreading in a real social network is described and investigated numerically. On the basis of data concerning amount of time devoted daily to social interactions, the influence of human activity on spreading process is investigated in the frame of SIRS model. It was found that the activity of an individual is positively correlated with its connectivity and the relation has power law form. The influence of control measures on the spreading process is investigated as a function of initial conditions. The cost-effectiveness of mass immunizations campaigns and target vaccinations is compared. It was found that the form of activity distribution has significant influence on the spreading phenomena in the network.

The influence of age-age correlations on epidemic spreading in social network

“…For an age greater than 20 years the A NN increases approximately linearly with age of an individual increasing. It should be noted that similar results we have found in on-line social networks [19]. The demographic structure in Poland is shown in Figure 1d.…”

Statistical properties of online avatar numbers in a massive multiplayer online role-playing game

“…Therefore, virtual worlds have great potential for research in the social, behavioral, and economic sciences [1]. For instance, we can design a kind of virus in a virtual world and let it spread to investigate its epidemics, we can design some economic games in a virtual world to study the formation of human cooperation (indeed, numerical experiments have been done [2]), and we can record the economic behaviors of avatars to understand the evolution of wealth distribution. A pioneering work was done by Edward Castronova, who traveled in a virtual world called "Norrath" and performed preliminary analysis of its economy [3].…”

A Literature Review of Social Network Analysis in Epidemic Prevention and Control

“…e impact of behavioral activities is divided into two parts, one is to promote the spread of the epidemic, and the other is to slow down or hinder the spread of the epidemic. On the one hand, Grabowski and Kosiński [111], Wang et al [112], Hancean et al [113], and Mbah et al [114] believed that human activities have a significant impact on the dynamic processes in social networks. ey found that the activity of an individual (the relative time spent interacting with others each day) is positively related to its degree.…”