Bayesian spatial models with application to HIV, TB and STI modeling in Kenya.
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Date
2014
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Abstract
This dissertation is concerned with developing and extending statistical models
in the area of spatial modeling with particular interest towards application to
HIV, TB and HSV-2 data. Hierarchical spatial modeling is a common and useful
approach for modeling complex spatially correlated data in many settings in epidemiological,
public health and ecological studies. Chapter 1 of this thesis gives
a chronological development of disease mapping models, from non-spatial to spatial
and from single disease models to multiple disease models. In Chapter 2, a
new model that relaxes the over-restrictive normal distribution assumption on the
spatially unstructured random effect by using the generalised Gaussian distribution
is introduced and investigated. The third chapter provides a framework for
including sampling weights into the Bayesian hierarchical disease mapping model.
In this model, design effect is used to re-scale the sample sizes. A new model for
over dispersed spatially correlated binary data is developed in chapter 4 of this
thesis; in this model, the over dispersion parameter is modeled by a beta random
effect which is allowed to vary spatially also. In chapter 5, the common multiple
spatial disease mapping models are reviewed and adopted for the binary data at
hand since the original models were developed based on Poisson count data. The
methodologies developed in this dissertation widen the toolbox for spatial analysis
and disease mapping in applications in epidemiology and public health studies.
Description
Ph. D. University of KwaZulu-Natal, Pietermaritzburg 2014.
Keywords
Bayesian statistical decision theory., Diagnosis--Statistical methods., Spatial analysis (Statistics)--Mathematical models., Public health--Statistical methods., Epidemiology--Statistical methods., Theses--Statistics.