The continuous-time approach is clearly in closer agreement with the ideal of standard disease models; however, utilising this method may be computationally prohibitive especially when large networks are involved. Discrete-time models may provide a viable alternative for three main reasons. Firstly, as the time steps involved in the discretetime model become sufficiently small, we would expect the two models to converge. Secondly, inaccuracies due to the discrete-time formulation are likely to be less substantial in network models compared to random-mixing models, providing two events do not occur in the same neighbourhood during the same time step. Finally, the daily cycle of contacts that regulate most of our lives means that using time steps of less than 24 hours may falsely represent the temporal accuracy that can be attributed to any simulation of the real world.