  We investigate what structural aspects of a collection of twelve empirical
temporal networks of human contacts are important to disease spreading. We scan
the entire parameter spaces of the two canonical models of infectious disease
epidemiology -- the Susceptible-Infectious-Susceptible (SIS) and
Susceptible-Infectious-Removed (SIR) models. The results from these simulations
are compared to reference data where we eliminate structures in the interevent
intervals, the time to the first contact in the data, or the time from the last
contact to the end of the sampling. The picture we find is that the birth and
death of links, and the total number of contacts over a link, are essential to
predict outbreaks. On the other hand, the exact times of contacts between the
beginning and end, or the interevent interval distribution, do not matter much.
In other words, a simplified picture of these empirical data sets that suffices
for epidemiological purposes is that links are born, is active with some
intensity, and die.
