A class of individual-level models (ILMs) outlined by R. Deardon et al., [Inference for individual level models of infectious diseases in large populations, Statist. Sin. 20 (2010), pp. 239-261] can be used to model the spread of infectious diseases in discrete time. The key feature of these ILMs is that they take into account covariate information on susceptible and infectious individuals as well as shared covariate information such as geography or contact measures. Here, such ILMs are fitted in a Bayesian framework using Markov chain Monte Carlo techniques to data sets from two studies on influenza transmission within households in Hong Kong during 2008 to 2009 and 2009 to 2010. The focus of this paper is to estimate the effect of vaccination on infection risk and choose a model that best fits the infection data.