  A key characteristic of the spread of infectious diseases is their ability to
use efficient transmission paths within contact graphs. This enables the
pathogen to maximise infection rates and spread within a target population. In
this work, we devise techniques to localise infections and decrease infection
rates based on a principled analysis of disease transmission paths within
human-contact networks (proximity graphs). Experimental results of disease
spreading shows that that at low visibility rates contact tracing slows disease
spreading. However to stop disease spreading, contact tracing requires both
significant visibility (at least 60%) into the proximity graph and the ability
to place half of the population under isolation. We find that pro-actively
isolating super-links -- key proximity encounters -- has significant benefits:
targeted isolation of a fourth of the population based on 35% visibility into
the proximity graph prevents an epidemic outbreak. It turns out that isolating
super-spreaders is more effective than contact tracing and testing but less
effective than targeting super-links. We highlight the important role of
topology in epidemic outbreaks. We argue that proactive innoculation of a
population by disabling super-links and super-spreaders may have an important
complimentary role alongside contact tracing and testing as part of a
sophisticated public-health response to epidemic outbreaks.
