  The Susceptible-Infectious-Recovered (SIR) model is the canonical model of
epidemics of infections that make people immune upon recovery. Many of the open
questions in computational epidemiology concern the underlying contact
structure's impact on models like the SIR model. Temporal networks constitute a
theoretical framework capable of encoding structures both in the networks of
who could infect whom and when these contacts happen. In this article, we
discuss the detailed assumptions behind such simulations -- how to make them
comparable with analytically tractable formulations of the SIR model, and at
the same time, as realistic as possible. We also present a highly optimized,
open-source code for this purpose and discuss all steps needed to make the
program as fast as possible.
