South Dakota State University
Spatial and Spatiotemporal Modeling of Epidemiological Data
Abstract
dc:description.abstract<p>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.</p>
Degree
thesis:*- Name thesis:degree_name
- Doctor of Philosophy (PhD)
- Level thesis:degree_level
- Dissertation - Open Access
- Discipline thesis:degree_discipline
- Mathematics and Statistics
- Year dc:date.available
- 2017
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Karki, Laxman
- Contributors dc:contributor
-
- Gary D. Hatfield
Subjects
dc:subject × 6Rights
dc:rights- Language dc:language
- en
Identifiers
dc:identifier.*- Repository record dc:identifier
- https://openprairie.sdstate.edu/etd/1215
- OAI identifier oai:identifier
- oai:openprairie.sdstate.edu:etd-2218