Infectious disease transmission can be influenced by spatial location, susceptibility risk factors, and other individual-level factors. Individual-level models have been developed to account for this complexity, however model-fitting can quickly become prohibitively computationally intensive as model complexity increases. This talk proposes a new method to reduce the computational burden for parameterizing individual-level models. Individual-level epidemic data are spatially aggregated, parameters are estimated from the aggregated data set, and the original data are then used to disaggregate the aggregate-level estimates for epidemic statistics to the individual level. We compare the performance of this so called “cluster-aggregate-disaggregate”, or CAD, method to that of traditional MCMC and approximate Bayesian computation. The performance of these methods is illustrated with both a simulation study and a data set from a 2001 U.K. foot and mouth disease outbreak amongst livestock.