A biologically inspired immunization strategy for network epidemiology.
Well-known immunization strategies, based on degree centrality, betweenness centrality, or closeness centrality, either neglect the structural significance of a node or require global information about the network. We propose a biologically inspired immunization strategy that circumvents both of these problems by considering the number of links of a focal node and the way the neighbors are connected among themselves. The strategy thus measures the dependence of the neighbors on the focal node, identifying the ability of this node to spread the disease. Nodes with the highest ability in the network are the first to be immunized. To test the performance of our method, we conduct numerical simulations on several computer-generated and empirical networks, using the susceptible-infected-recovered (SIR) model. The results show that the proposed strategy largely outperforms the existing well-known strategies.
School of Computer and Information Science, Southwest University, Chongqing 400715, China.
Yang
Y
Liu
School of Computer and Information Science, Southwest University, Chongqing 400715, China; School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China. Electronic address: prof.deng@hotmail.com.
Yong
Y
Deng
Center of Mathematics for Social Creativity, Hokkaido University, Sapporo 060-0812, Japan. Electronic address: mjusup@gmail.com.
Marko
M
Jusup
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 816-8580, Japan. Electronic address: zhenwang0@gmail.com.
Zhen
Z
Wang
D000465:Algorithms; D003141:Communicable Diseases / Q000453:epidemiology / Q000276:immunology*; D003195:Computer Communication Networks; D003198:Computer Simulation; D058872:Epidemics / Q000517:prevention & control*; D006801:Humans; D007114:Immunization / Q000379:methods*; D008962:Models, Theoretical; D016839:Physarum polycephalum / Q000254:growth & development
Betweenness centrality; Closeness centrality; Degree centrality; Heterogeneous topology; Infectious agent; Physarum polycephalum; SIR model