Predicting and containing epidemic risk using friendship networks

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. Through computer simulation, we compare infection processes on the resulting networks and show that friendship provides a poor identification of the individuals at risk if the infection is driven by physical encounter. This result is not driven by the static nature of the friendship network opposed to the time-varying nature of the encounter network, as a static version of the encounter network provides more accurate prediction of risk than the friendship network. Despite this limit, the information enclosed in the friendship network can be leveraged for monitoring and containment of epidemics. In particular, we show that periodical and relatively infrequent monitoring of the 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.