Statistical models are used to identify infectious disease dynamics. It is typically
assumed that only distance between susceptible and infectious individuals is important for modeling regardless of the spatial location of the individuals. Geographically-dependent individual-level models (GD-ILMs) are used which allow us to consider the
effect of the spatial locations of individuals, and also the distance between susceptible
and infectious individuals for determining the risk of infection. In these models, it is assumed that the covariates used to predict the outbreak and transmission of diseases are measured accurately. However, there are many applications that the covariates are prone to measurement error. For instance, census covariates such as indigenous and socio-economic status, which are measured with error, are used as risk factors for influenza. In this talk, we propose a GD-ILM which also accounts for the individual-level
and area-level covariates measurement error. Monte Carlo Expectation Conditional
Maximization (MCECM) algorithm is used for inference. Estimated parameters using
MCECM enable us to predict the areas with the highest average infectivity rates. We
evaluate the performance of the proposed approach through simulation studies and
also by a real data application of influenza data in Manitoba, Canada.