  Infectious disease models can be of great use for understanding the
underlying mechanisms that influence the spread of diseases and predicting
future disease progression. Modeling has been increasingly used to evaluate the
potential impact of different control measures and to guide public health
policy decisions. In recent years, there has been rapid progress in developing
spatio-temporal modeling of infectious diseases and an example of such recent
developments is the discrete time individual-level models (ILMs). These models
are well developed and provide a common framework for modeling many disease
systems, however, they assume the probability of disease transmission between
two individuals depends only on their spatial separation and not on their
spatial locations. In cases where spatial location itself is important for
understanding the spread of emerging infectious diseases and identifying their
causes, it would be beneficial to incorporate the effect of spatial location in
the model. In this study, we thus generalize the ILMs to a new class of
geographically-dependent ILMs (GD-ILMs), to allow for the evaluation of the
effect of spatially varying risk factors (e.g., education, environmental), as
well as unobserved spatial structure, upon the transmission of infectious
disease. Specifically, we consider a conditional autoregressive model to
capture the effects of unobserved spatially structured latent covariates or
measurement error. This results in flexible infectious disease models that can
be used for formulating etiological hypotheses and identifying geographical
regions of unusually high risk to formulate preventive action. The reliability
of these models are investigated on a combination of simulated epidemic data
and Alberta seasonal influenza outbreak data (2009). This new class of models
is fitted to data within a Bayesian statistical framework using MCMC methods.
