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- 4Generalized linear mixed model
- 2Conditional autoregressive
- 2Geographic epidemiology
- 1Animal movement
- 1Campylorhynchus rufinucha
- 1Biological Sciences, Department of
- 1Biological Sciences, Department of/Journal Articles (Biological Sciences)
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This paper studies generalized linear mixed models (GLMMs) for the analysis of geographic and temporal variability of disease rates. This class of models adopts spatially correlated random effects and random temporal components. Spatio-temporal models that use conditional autoregressive smoothing...
The persistence of forest-dependent species in fragmented landscapes is fundamentally linked to the movement of individuals among subpopulations. The paths taken by dispersing individuals can be considered a series of steps built from individual route choices. Despite the importance of these...
Timely, accurate predictions of potential influenza epidemics are essential for healthcare providers and policy makers as the epidemics can result in heavy demands for health services. Current statistical modeling of surveillance data has limited prediction abilities and often fails to respond...
To analyze childhood cancer diagnoses in the province of Alberta, Canada during 1983-2004, we construct a generalized linear mixed model for the analysis of geographic and temporal variability of cancer rates. In this model, spatially correlated random e®ects and temporal components are adopted....