  Records of social interactions provide us with new sources of data for
understanding how interaction patterns affect collective dynamics. Such human
activity patterns are often bursty, i.e., they consist of short periods of
intense activity followed by long periods of silence. This burstiness has been
shown to affect spreading phenomena; it accelerates epidemic spreading in some
cases and slows it down in other cases. We investigate a model of
history-dependent contagion. In our model, repeated interactions between
susceptible and infected individuals in a short period of time is needed for a
susceptible individual to contract infection. We carry out numerical
simulations on real temporal network data to find that bursty activity patterns
facilitate epidemic spreading in our model.
