Physical encounter is the most common vehicle for the spread of infectious diseases, but detailed information about said encounters is often unavailable because expensive, unpractical to collect, or privacy sensitive. The present work asks whether the friendship ties between the individuals in a social network can be used to successfully predict and contain epidemic 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 their reported friendship – the encounter network and the friendship network.