Halting viruses in scale-free networks.

By Z. Dezső, Albert-László Barabási. Physical review. E, Statistical, nonlinear, and soft matter physics, 2001

610 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e3c0715bd032e125b24e451872fb2c8840adf0a2

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In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

Citations in the corpus

Identifying influential spreaders in complex networks (2010, neutral): “Previous studies have shown that the largest persistence is found in the network hubs, which are re-infected frequently owing to the large number of neighbour”
On the approximability of influence in social networks (2008, methodology): “Which set of individuals should we target (for instance, one form of targeting involves o.ering free samples of the product)?”
Social Network Sensors for Early Detection of Contagious Outbreaks (2010, background): “Prior modeling research suggests that vaccinating central individuals in networks could enhance the population-level efficacy of a prophylactic intervention (9-12) and that optimal placement of sensors in physical networks (such as water pumping stations) could detect outbreaks sooner.”
Critical phenomena in complex networks (2007, background): “A very popular topic is various immunization strategies, see Cohen et al. (2003b); Dezsõ and Barabási (2002); Gallos et al. (2007a); Pastor-Satorras and Vespignani (2002b, 2003), and many other works.”
Epidemic spreading in real networks: an eigenvalue viewpoint (2003, background): “IIS-9817496, IIS-9988876, IIS0083148, IIS-0113089, IIS-0209107 IIS-0205224 by the Pennsylvania Infrastructure Technology Alliance (PITA) Grant No. 22-901-0001, and by the Defense Advanced Research Projects Agency under Contract No.”
Debiasing social wisdom (2013, background): “Budgeted actions and inquiries over social networks have been studied before in the context of inﬂuence maximization [7, 15], vaccination [6] and expectation polling [4].”
When individual behaviour matters: homogeneous and network models in epidemiology (2007, background): “The field has focused particularly on scale-free random networks (May & Lloyd 2001; Dezso & Barabasi 2002; Pastor-Satorras & Vespignani 2002), based on the apparent ubiquity of such networks in natural and human-made systems and a (limited) set of studies of epidemiologically relevant contact patt”
Statistical physics of vaccination (2016, background): “Thus, if we vaccinate only the nodes whose degree exceeds a preselected threshold value kc, it should be easy to decrease the variance and increase the epidemic threshold [275, 282, 293].”
Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications (2005, background): “Furthermore, the hublike structure of SF graphs is such that the epidemic threshold is zero for contagion phenomena [72, 12, 74, 73], thus suggesting that the natural way to stop epidemics, either for computer viruses/worms or biological epidemics such as AIDS, is to protect these hubs [34, 25].”
Extremal Properties of Random Structures (2003, neutral): “idemic propagation on scale free networks found that the threshold for an epidemic is zero, implying that a ﬁnite fraction of the graph becomes infected for arbitrarily low transmission probabilities [11, 39, 34]. The presence of additional network structure was found to further inﬂuence the spread ”