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Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/7631

Title: A Spatial Statistical Analysis to Estimate the Spatial Dynamics of the 2009 H1N1 Pandemic in the Greater Toronto Area
Authors: Fan, WENYONG

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Keywords: Spatial Statistics, Spatial Dynamics, H1N1 Pandemic, Spatial Autocorrelation
Issue Date: 5-Nov-2012
Series/Report no.: Canadian theses
Abstract: The 2009 H1N1 pandemic caused serious concerns worldwide due to the novel biological feature of the virus strain, and the high morbidity rate for youth. The urban scale is crucial for analyzing the pandemic in metropolitan areas such as the Greater Toronto Area (GTA) of Canada because of its large population. The challenge of exploring the spatial dynamics of H1N1 is exaggerated by data scarcity and the absence of an immediately applicable methodology at such a scale. In this study, a stepwise methodology is developed, and a retrospective spatial statistical analysis is conducted using the methodology to estimate the spatial dynamics of the 2009 H1N1 pandemic in the GTA when the data scarcity exists. The global and local spatial autocorrelation analyses are carried out through the use of multiple spatial analysis tools to confirm the existence and significance of spatial clustering effects. A Generalized Linear Mixed Model (GLMM) implemented in Statistical Analysis System (SAS) 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 non-spatial model respectively. Comparing the results of spatial and non-spatial configurations of the GLMM suggests that the spatial GLMM, which incorporates the ICAR model, proves a better predictability. This indicates that the methodology developed in this study can be applied to epidemiology studies to analyze the spatial dynamics in similar scenarios.
Description: Thesis (Master, Geography) -- Queen's University, 2012-10-30 17:41:28.445
URI: http://hdl.handle.net/1974/7631
Appears in Collections:Queen's Graduate Theses and Dissertations
Department of Geography and Planning Graduate Theses

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