  The spread of an infection on a real-world social network is determined by
the interplay of two processes: the dynamics of the network, whose structure
changes over time according to the encounters between individuals, and the
dynamics on the network, whose nodes can infect each other after an encounter.
Physical encounter is the most common vehicle for the spread of infectious
diseases, but detailed information about encounters is often unavailable
because expensive, unpractical to collect or privacy sensitive. We asks whether
the friendship ties between the individuals in a social network successfully
predict who is at risk. Using a dataset from a popular online review service,
we build a time-varying network that is a proxy of physical encounter between
users and a static network based on reported friendship. Through computer
simulations, we compare infection processes on the resulting networks and show
that, whereas distance on the friendship network is correlated to epidemic
risk, friendship provides a poor identification of the individuals at risk if
the infection is driven by physical encounter. Such limit is not due to the
randomness of the infection, but to the structural differences of the two
networks. In contrast to the macroscopic similarity between processes spreading
on different networks, the differences in local connectivity determined by the
two definitions of edges result in striking differences between the dynamics at
a microscopic level. Despite the limits highlighted, we show that periodical
and relatively infrequent monitoring of the real infection on the encounter
network allows to correct the predicted infection on the friendship network and
to achieve satisfactory prediction accuracy. In addition, the friendship
network contains valuable information to effectively contain epidemic outbreaks
when a limited budget is available for immunization.
