Publications (4)0 Total impact
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Article: Epidemic reemergence in adaptive complex networks
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ABSTRACT: The dynamic nature of system gives rise to dynamical features of epidemic spreading, such as oscillation and bistability. In this paper, by studying the epidemic spreading in growing networks, in which susceptible nodes may adaptively break the connections with infected ones yet avoid getting isolated, we reveal a new phenomenon - \emph{epidemic reemergence}, where the number of infected nodes is incubated at a low level for a long time and then bursts up for a short time. The process may repeat several times before the infection finally vanishes. Simulation results show that all the three factors, namely the network growth, the connection breaking and the isolation avoidance, are necessary for epidemic reemergence to happen. We present a simple theoretical analysis to explain the process of reemergence in detail. Our study may offer some useful insights helping explain the phenomenon of repeated epidemic explosions.03/2012; -
Article: Effects of fear factors in disease propagation
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ABSTRACT: Upon an outbreak of a dangerous infectious disease, people generally tend to reduce their contacts with others in fear of getting infected. Such typical actions apparently help slow down the spreading of infection. Thanks to today's broad public media coverage, the fear factor may even contribute to prevent an outbreak from happening. We are motivated to study such effects by adopting a complex network approach. Firstly we evaluate the simple case where connections between individuals are randomly removed due to fear factor. Then we consider a different case where each individual keeps at least a few connections after contact reduction. Such a case is arguably more realistic since people may choose to keep a few social contacts, e.g., with their family members and closest friends, at any cost. Finally a study is conducted on the case where connection removals are carried out dynamically while the infection is spreading out. Analytical and simulation results show that the fear factor may not easily prevent an epidemic outbreak from happening in scale-free networks. However, it significantly reduces the fraction of the nodes ever getting infected during the outbreak.12/2011; -
Conference Proceeding: A Preliminary Study on the Effects of Fear Factors in Disease Propagation.
Complex Sciences, First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 2; 01/2009 -
Article: Robustness of complex communication networks under link attacks
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ABSTRACT: Recent research results show that some most important complex communication systems, which usually can be modeled as scale-free networks with a power-law nodal degree distribution, may be fragile under intentional attacks that take down network hubs. We study the robustness of these networks under deliberate attacks which remove network links. Specifically, we evaluate the extreme case where an efficient graph-partitioning algorithm is applied, based on accurate network-topology information, to decide on the links to be removed. Simulation results show that even such type of calculated link-removal attack cannot easily split a complex communication network. Moreover, among the two split parts, the larger one generally remains as a scale-free network with a very small network diameter. We also consider the case where a specific set of nodes have to be split away from the major part of the network. Simulation results show that applying a graph-partitioning algorithm generally does not lead to a significantly more cost-effective solution than simply removing the given set of nodes together with the links connected to them.
