Spatial Analysis and Statistical Modeling of 2009 H1N1 Pandemic in the Greater Toronto Area

Urban scale is crucial for analyzing a pandemic in metropolitan areas such as the Greater Toronto Area (GTA) of Canada because of its large population. In this study, a stepwise methodology is developed to estimate the spatial dynamics of the H1N1 pandemic in the GTA when data scarcity exists. A retrospective spatial statistical analysis is conducted using the methodology to estimate the spatial dynamics of the 2009 H1N1 pandemic in the GTA. Global and local spatial autocorrelation analyses are carried out to check the existence and significance of spatial clustering effects. Then a Generalized Linear Mixed Model (GLMM) is used to estimate the area‐specific spatial dynamics. The GLMM is configured to a spatial model that incorporates an Intrinsic Gaussian Conditionally Autoregressive (ICAR) model and a nonspatial model, respectively. Comparing the results of spatial and nonspatial configurations of the GLMM suggests that the spatial GLMM, which incorporates the ICAR model, proves to be more predictable.