Dissertation - Open Access
Doctor of Philosophy (PhD)
Mathematics and Statistics
Gary D. Hatfield
diabetes, GWR, INLA, Lyme disease, MGWR
This dissertation focuses on modeling approach for spatial and spatiotemporal data with epidemiological applications. Chapter one gives the general overview of spatial and spatiotemporal data and challenges in the statistical analysis of spatial and spatiotemporal data, and motivation and objectives of the study. Chapter two describes the regression models commonly used in spatial data analysis. Various types of regression methods such as OLS, GWR and MGWR were used to study the association between diabetes prevalence and socioeconomic and lifestyle factors on county level data of Midwestern United States. A new analysis workflow is purposed for regression analysis of spatial data. Chapter three describes recently developed INLA as an alternative of traditionally used MCMC in Bayesian hierarchical models. INLA method was used to identify the best regression model for the spatiotemporal regression analysis of Lyme disease count data with climatic covariates in county-level data in Minnesota. Chapter four gives the contribution of this dissertation and discusses the direction for the future research.
Library of Congress Subject Headings
Spatial analysis (Statistics)
Diabetes -- Middle West.
Lyme disease -- Minnesota.
Includes bibliographical references
Number of Pages
South Dakota State University
Copyright © Laxman Karki
Karki, Laxman, "Spatial and Spatiotemporal Modeling of Epidemiological Data" (2017). Theses and Dissertations. 1215.