Latent Conditional Individual-Level Models for Infectious Disease Modeling

Individual-level models (ILMs) have previously been used to model the spatiotemporal spread of infectious diseases. These models can incorporate individual-level covariate information, to account for population heterogeneity. However, incomplete or unreliable data are a common problem in infectious disease modeling, and models that are explicitly dependent on such information may not be robust to these inherent uncertainties. In this investigation, we assess an adaptation to a spatial ILM that incorporates a latent grouping structure based on some trait heterogeneous in the population. The resulting latent conditional ILM is then only dependent upon a discrete latent grouping variable, rather than precise covariate information. The posterior predictive ability of this proposed model is tested through a simulation study, in which the model is fitted to epidemic data simulated from a true model that utilizes explicit covariate information. In addition, the posterior predictive ability of the proposed ILM is also compared to that of an ILM that assumes population homogeneity. The application of these models to data from the 2001 UK foot-and-mouth disease epidemic is also explored. This study demonstrates that the use of a discrete latent grouping variable can be an effective alternative to utilizing covariate information, particularly when such information may be unreliable.