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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Network epidemiology has become a vital tool in understanding the effects ofhigh-degree vertices, geographic and demographic communities, and otherinhomogeneities in social structure on the spread of disease. However, manynetworks derived from modern datasets are quite dense, such as mobilitynetworks where each location has links to a large number of potentialdestinations. One way to reduce the computational effort of simulatingepidemics on these networks is sparsification, where we select a representativesubset of edges based on some measure of their importance. Recently an approachwas proposed using an algorithm based on the effective resistance of the edges.We explore how effective resistance is correlated with the probability that anedge transmits disease in the SI model. We find that in some cases these twonotions of edge importance are well correlated, making effective resistance acomputationally efficient proxy for the importance of an edge to epidemicspread. In other cases, the correlation is weaker, and we discuss situations inwhich effective resistance is not a good proxy for epidemic importance.

Contagion-Preserving Network Sparsifiers: Exploring Epidemic Edge Importance Utilizing Effective Resistance