Using daily infection data for Hong Kong we explore the validity of a variety of models of disease propagation when applied to the SARS epidemic. Surrogate data methods show that simple random models are insufficient and that the standard epidemic susceptible-infected-removed model does not fully account for the underlying variability in the observed data. As an alternative, we consider a more complex small world network model and show that such a structure can be applied to reliably produce simulations quantitative similar to the true data. The small world network model not only captures the apparently random fluctuation in the reported data, but can also reproduce mini-outbreaks such as those caused by so-called "super-spreaders" and in the Hong Kong housing estate of Amoy Gardens.