Temporal network structures controlling disease spreading

TL;DR

This study analyzes how temporal structures in human contact networks significantly influence disease spreading, more than static network representations, highlighting the importance of considering time-varying interactions.

Contribution

It demonstrates that temporal network structures have a greater impact on disease dynamics than static or fully connected models, emphasizing the role of temporal features.

Findings

Temporal networks differ from static networks in disease spread outcomes.

Long-term temporal features like node and link turnover are crucial.

Temporal structures influence outbreak size and extinction time more than static topology.

Abstract

We investigate disease spreading on eight empirical data sets of human contacts (mostly proximity networks recording who is close to whom, at what time). We compare three levels of representations of these data sets: temporal networks, static networks and a fully connected topology. We notice that the difference between the static and fully-connected networks -- with respect to time to extinction and average outbreak size -- is smaller than between the temporal and static topologies. This suggests that, for these data sets, temporal structures influence disease spreading more than static network structures. To explain the details in the differences between the representations, we use 32 network measures. This study concur that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics.