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dc.contributor.authorFan, Wenyongen
dc.date2012-10-30 17:41:28.445
dc.date.accessioned2012-11-05T21:30:20Z
dc.date.available2012-11-05T21:30:20Z
dc.date.issued2012-11-05
dc.identifier.urihttp://hdl.handle.net/1974/7631
dc.descriptionThesis (Master, Geography) -- Queen's University, 2012-10-30 17:41:28.445en
dc.description.abstractThe 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.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectSpatial Statisticsen
dc.subject.classificationSpatial Dynamics
dc.subject.classificationH1N1 Pandemic
dc.subject.classificationSpatial Autocorrelation
dc.titleA Spatial Statistical Analysis to Estimate the Spatial Dynamics of the 2009 H1N1 Pandemic in the Greater Toronto Areaen
dc.typethesisen
dc.description.degreeM.Sc.en
dc.contributor.supervisorChen, Dongmeien
dc.contributor.departmentGeographyen
dc.degree.grantorQueen's University at Kingstonen


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