A class of complex statistical models, known as individual-level models, has been effectively used to model the spread of infectious diseases. These models are often fitted within a Bayesian Markov chain Monte Carlo framework, which can have a significant computational expense due to the complex nature of the likelihood function associated with this class of models. We first explored the effect of reducing the overall population size by aggregating the data into spatial clusters on the computational expense of the model fitting procedure. Reparameterized individual-level models were then fit to the spatially aggregated data, and the ability of these reparameterized individual-level models to identify a covariate effect, when fitted to this reduced data set, was investigated through a simulation study. This approach was then expanded upon by investigating methods to disaggregate the spatial clusters and model the spread of the epidemic back on the level of the individual. The ability of this “reverse aggregation” approach to capture epidemic summary statistics was investigated, suggesting that while there was a tendency to overestimate some epidemic statistics, this approach may be a reasonable alternative when it is necessary to reduce the computational expense of the model fitting procedure.

This work was done in collaboration with M. Ward (University of Guelph) and R. Deardon (University of Calgary). This work was supported by an NSERC Discovery Grant.