Halting viruses in scale-free networks.
TLDR
It is demonstrated that policies that discriminate between the nodes, curing mostly the highly connected nodes, can restore a finite epidemic threshold and potentially eradicate a virus.Abstract:
The vanishing epidemic threshold for viruses spreading on scale-free networks indicate that traditional methods, aiming to decrease a virus' spreading rate cannot succeed in eradicating an epidemic. We demonstrate that policies that discriminate between the nodes, curing mostly the highly connected nodes, can restore a finite epidemic threshold and potentially eradicate a virus. We find that the more biased a policy is towards the hubs, the more chance it has to bring the epidemic threshold above the virus' spreading rate. Furthermore, such biased policies are more cost effective, requiring less cures to eradicate the virus.read more
Citations
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Journal ArticleDOI
Epidemic processes in complex networks
TL;DR: A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear.
Journal ArticleDOI
Identification of influential spreaders in complex networks
Maksim Kitsak,Maksim Kitsak,Lazaros K. Gallos,Shlomo Havlin,Fredrik Liljeros,Lev Muchnik,H. Eugene Stanley,Hernán A. Makse +7 more
TL;DR: This paper showed that the most efficient spreaders are not always necessarily the most connected agents in a network, and that the position of an agent relative to the hierarchical topological organization of the network might be as important as its connectivity.
Journal ArticleDOI
Critical phenomena in complex networks
TL;DR: A wide range of critical phenomena in equilibrium and growing networks including the birth of the giant connected component, percolation, $k$-core percolations, phenomena near epidemic thresholds, condensation transitions,critical phenomena in spin models placed on networks, synchronization, and self-organized criticality effects in interacting systems on networks are mentioned.
Book
Random graph dynamics
TL;DR: The Erdos-Renyi random graphs model, a version of the CHKNS model, helps clarify the role of randomness in the distribution of values in the discrete-time world.
Proceedings ArticleDOI
Epidemic spreading in real networks: an eigenvalue viewpoint
TL;DR: A general epidemic threshold condition that applies to arbitrary graphs is proposed and it is proved that, under reasonable approximations, the epidemic threshold for a network is closely related to the largest eigenvalue of its adjacency matrix.
References
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Infectious Diseases of Humans: Dynamics and Control
Roy M. Anderson,Robert M. May +1 more
TL;DR: This book discusses the biology of host-microparasite associations, dynamics of acquired immunity heterogeneity within the human community indirectly transmitted helminths, and the ecology and genetics of hosts and parasites.
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Diffusion and Reactions in Fractals and Disordered Systems
Daniel ben-Avraham,Shlomo Havlin +1 more
TL;DR: In this paper, the authors present an exact solvable model of coalescence and the IPDF method to represent the dynamics of random walks and diffusion in the Sierpinski gasket.