With The Complex Network Of Infectious Disease Model, Predict Risk

We study by analytical methods and large scale simulations a dynamical model forthe spreading of epidemics in complex networks. Opposite to standard models, epidemicprocesses in these Epidemics and immunization in scale-free networks networks do not possessany epidemic threshold below which the infection cannot produce a major epidemic outbreakor an endemic state. In principle, SF networks are prone to the persistence of diseaseswhatever infective rate they may have.We investigate the e?ect of risk perception in a simplemodel of epidemic spreading.We assume that the perception of the risk of being infecteddepends on the fraction of neighbors that are ill. In this paper,we study dynamical modelwith risk perception. This paper is composed of four chapters.In the first chapter, we introduce the signification of epidemic model exploration . Inchapter 2,introduce the knowledge of the basic dynamical model for the spreading of epi-demics in complex networks and concepts as well as main model forms of epidemic dynam-ics.In addition, the main work of this paper is also simply introduced in this chapter.In chapter 3, work out the epidemic threshold with risk perception.The epidemic thresh-old has the relationship with risk perception.It was assumed that the larger the node de-gree,the greater the infectivity of the node,and the infectivity is equal to the node de-gree.Then,epidemic threshold is 0.When everyone has the same infectivity 1+ab,the epidemicthreshold isλc = 1a+mb . In chapter 4, we study a dynamical model for the spreading withhosts.Diseases spread not only by contacts between individuals,but also by contacts betweenindividuals and media.The results show that the epidemic threshold is smaller with media.