Limits of Friendship Networks in Predicting Epidemic Risk

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 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 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 their reported friendship. 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. Our analyses suggest that such limit is not due to the randomness of the infection process, but to the structural differences of the two networks. In addition, we argue that our results are not driven by the static nature of the friendship network as opposed to the time-varying nature of the encounter network. In contrast to the macroscopic similarity between processes spreading on different networks – confirmed by our simulations, the differences in local connectivity determined by the two definitions of edges result in striking differences between the dynamics at a microscopic level, preventing the identification of the nodes at risk.