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Epidemic Dynamics with Feedback Mechanism in Exponential Networks ∗    ),YAN Jia-Ren(   ) † ,ZHANG Jian-Guo(   ),WANG Li(  ) LIU Zi-Ran( Department of physics,Hunan Normal University,Changsha 410081 Abstract In this letter, we introduce a feedback mechanism to study the spreading of epidemic by analytical methods and large scale simulations in exponential networks. We find that the introducing of the feedback mechanism can reduce the density of infected individuals. Furthermore, it doesn’t change the epidemic threshold (critical point) λc . These results can help us understand epidemic spreading phenomena on social networks more practically and design appropriate strategies to control social infections. PACS: 89.75.Fb, 87.23.Ge, 64.60.Ht Recently, there has been a burst of activities in the study of complex networks, and a lot of significant work has been done.[1−3] One of the principle practical applications of the work has been in modeling the spread of epidemic disease.[4−9] A detailed understanding of the structure of these networks and dynamics of disease upon them is crucial to the development of strategies for disease controlling.[10−12] Much completed work is based on the unchangeable structures of models.[4−12] However, while there is a disease spreading in the community, being afraid of infected, people will reduce the frequency of going out to public places consciously. The larger value of density of infected individuals and the more lethal of the disease will result in less communications. The structures of networks will be changed according to the feedback information.[13] So we introduce a feedback mechanism[14] to study epidemic dynamics in expon