English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

During an epidemic, individual nodes in a network may adapt their connectionsto reduce the chance of infection. A common form of adaption is avoidancerewiring, where a noninfected node breaks a connection to an infected neighborand forms a new connection to another noninfected node. Here we explore theeffects of such adaptivity on stochastic fluctuations in thesusceptible-infected-susceptible model, focusing on the largest fluctuationsthat result in extinction of infection. Using techniques from large-deviationtheory, combined with a measurement of heterogeneity in the susceptible degreedistribution at the endemic state, we are able to predict and analyze largefluctuations and extinction in adaptive networks. We find that in the limit ofsmall rewiring there is a sharp exponential reduction in mean extinction timescompared to the case of zero adaption. Furthermore, we find an exponentialenhancement in the probability of large fluctuations with increased rewiringrate, even when holding the average number of infected nodes constant.

Enhancement of large fluctuations to extinction in adaptive networks