Individual level models (ILMs) have been applied to infectious epidemic data to understand spatiotemporal dynamics of infectious diseases. Typically, these models are analysed in a Bayesian framework using Markov chain Monte Carlo (MCMC). We test the effect of misspecifying the latent and infectious period of an infectious disease in the model. This is done by simulating data from a true model, and fitting various misspecified models. Consequences are examined through two measures: first, we observe how the basic reproduction number, 'R'0, behaves as latent and infectious periods are varied; second, how optimal vaccination strategies: developed via simulation studies, are affected. As latent and infectious periods become more misspecified there is significant deviation in estimates of ' R'0 from its true value. Where the misspecification results in a higher 'R'0 estimate, we naturally find that there needs to be a more stringent vaccination policy, and vice versa.