Modeling and analysis of epidemic spreading on community network with node's birth and death

Abstract In this paper, a modified susceptible infected susceptible (SIS) epidemic model is proposed on community structure networks considering birth and death of node. For the existence of node's death would change the topology of global network, the characteristic of network with death rate is discussed. Then we study the epidemiology behavior based on the mean-field theory and derive the relationships between epidemic threshold and other parameters, such as modularity coefficient, birth rate and death rates (caused by disease or other reasons). In addition, the stability of endemic equilibrium is analyzed. Theoretical analysis and simulations show that the epidemic threshold increases with the increase of two kinds of death rates, while it decreases with the increase of the modularity coefficient and network size.

[1]  J. Liu,et al.  The spread of disease with birth and death on networks , 2004, q-bio/0402042.

[2]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Guanrong Chen,et al.  Epidemic spreading on contact networks with adaptive weights. , 2013, Journal of theoretical biology.

[4]  Zonghua Liu,et al.  How community structure influences epidemic spread in social networks , 2008 .

[5]  Alessandro Vespignani,et al.  Epidemic dynamics and endemic states in complex networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Stefania Ottaviano,et al.  Epidemic outbreaks in two-scale community networks. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[8]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[9]  Shuigeng Zhou,et al.  Epidemic spreading in weighted scale-free networks with community structure , 2009 .

[10]  Guoping Jiang,et al.  Traffic Driven Epidemic Spreading in Homogeneous Networks with Community Structure , 2012, J. Networks.

[11]  Mo Li,et al.  IODetector: a generic service for indoor outdoor detection , 2012, SenSys '12.

[12]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

[13]  Pietro Liò,et al.  Community Structure in Social Networks: Applications for Epidemiological Modelling , 2011, PloS one.

[14]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Michael Small,et al.  Staged progression model for epidemic spread on homogeneous and heterogeneous networks , 2011, J. Syst. Sci. Complex..

[16]  Bambi Hu,et al.  Epidemic spreading in community networks , 2005 .

[17]  Zhi-Hong Guan,et al.  A stochastic SIR epidemic on scale-free network with community structure , 2013 .

[18]  Zhen Jin,et al.  Epidemic spreading on complex networks with community structure , 2012, Appl. Math. Comput..

[19]  Wei Huang,et al.  Epidemic spreading in scale-free networks with community structure , 2007 .

[20]  Y. Moreno,et al.  Spreading of persistent infections in heterogeneous populations. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Hartmut Neven,et al.  PhotoOCR: Reading Text in Uncontrolled Conditions , 2013, 2013 IEEE International Conference on Computer Vision.

[22]  Antti Ylä-Jääski,et al.  Characterize energy impact of concurrent network-intensive applications on mobile platforms , 2013, MobiArch '13.

[23]  Guanrong Chen,et al.  Global attractivity of a network-based epidemic SIS model with nonlinear infectivity , 2012 .