Individual-level models are a class of complex statistical models, often fitted within a Bayesian Markov chain Monte Carlo framework, that have been effectively used to model the spread of infectious diseases. The ability of these models to incorporate individual-level covariate information allows them to be highly flexible, and to account for such characteristics as population heterogeneity. However, these models can be subject to inherent uncertainties often found in infectious disease data. As well, their complex nature can lead to a significant computational expense when fitting these models to epidemic data, particularly for large populations. An individual-level model that incorporates a latent grouping structure into the modeling procedure, based on some heterogeneous population characteristics, is investigated. The dependence of this latent conditional individual-level model on a discrete latent grouping variable alleviates the need for explicit, although possibly unreliable, covariate information. A simulation study is used to assess the posterior predictive ability of this model, in comparison to individual-level models that utilize the full covariate information, or that assume population homogeneity. These models are also applied to data from the 2001 UK foot-and-mouth disease epidemic. When attempting to compare complex models fitted within the Bayesian framework, the identification of appropriate model selection tools would be beneficial. The use of deviance information criterion (DIC) as model comparison tool, particularly for the latent conditional individual-level models, is investigated. A simulation study is used to compare five variants of the DIC, and the ability of each DIC variant to select the true model is determined. Finally, an investigation into methods to reduce the computational burden associated with individual-level models is carried out, based on an individual-level model that also incorporates population heterogeneity through a discrete grouping variable. A simulation study is used to determine the effect of reducing the overall population size by aggregating the data into spatial clusters. Reparameterized individual-level models, accounting for the aggregation effect, are fitted to the aggregated data. The effect of data aggregation on the ability of two reparameterized individual-level models to identify a covariate effect, as well as on the computational expense of the model fitting procedure, is explored.

Infectious disease models can be used to understand mechanisms of the spread of diseases and thus, may effectively guide control policies for potential outbreaks. Deardon et al. (2010) introduced a class of individual-level models (ILMs) which are highly flexible. Parameter estimates for ILMs can be achieved by means of Markov chain Monte Carlo (MCMC) methods within a Bayesian framework. Here, we introduce an extended form of ILM, described by Deardon et al. (2010), and compare this model with the original ILM in the context of a simple spatial system. The two spatial ILMs are fitted to 70 simulated data sets and a real data set on tomato spotted wilt virus (TSWV) in pepper plants (Hughes et al., 1997). We find that the modified ILM is more flexible than the original ILM and may fit some data sets better.

Veterinarians often perform controlled experiments in which they inoculate animals with infectious diseases. They then monitor the transmission process in infected animals. The aim of such experiments can be to assess vaccine effects. The fitting of individual-level models (ILMs) to the infectious disease data, typically achieved by means of Markov Chain Monte Carlo (MCMC) methods, can be computationally burdensome. Here, we want to see if a vaccination effect can be identified using simpler regression-type models rather than the complex infectious disease models. We examine the use of various logistic growth curve models, via a series of simulated experiments in which the underlying true model is a mechanistic model of infectious disease spread. We want to investigate whether a vaccination effect can be identified when only partial epidemic curves are observed, and to assess the performance of these models when experiments are run with various sets of observational times.