  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.

Populations and Evolution;Physics and Society