  We study the contact process on a class of evolving scale-free networks,
where each node updates its connections at independent random times. We give a
rigorous mathematical proof that there is a transition between a phase where
for all infection rates the infection survives for a long time, at least
exponential in the network size, and a phase where for sufficiently small
infection rates extinction occurs quickly, at most like the square root of the
network size. The phase transition occurs when the power-law exponent crosses
the value four. This behaviour is in contrast to that of the contact process on
the corresponding static model, where there is no phase transition, as well as
that of a classical mean-field approximation, which has a phase transition at
power-law exponent three. The new observation behind our result is that
temporal variability of networks can simultaneously increase the rate at which
the infection spreads in the network, and decrease the time which the infection
spends in metastable states.
