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.