Browsing by Author "Owino, Ngesa Oscar."

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Bayesian spatial joint and spatial-temporal disease modeling with application to HIV, HSV-2 and Malaria using case studies from Kenya and Angola respectively.
(2017) Okango, Elphas Luchemo.; Mwambi, Henry Godwell.; Owino, Ngesa Oscar.
In this thesis we develop and extend existing statistical models for spatial disease modeling and apply them to HIV, HSV-2 and malaria data. The availability of geo-referenced data and free software has seen many disease mapping models developed and applied in epidemiology, public health, agriculture and ecology among other areas. In chapter 1 we provide a background and developments in the field of disease mapping. We present in brief some limiting assumptions and how recent developments have tried to relax them. Chapter 2 introduces a model; the semi-parametric joint model to model HIV and HSV-2. The semi-parametric joint model performed better than the single models in terms of DIC. The limiting linearity assumption was relaxed by using the penalized regression splines for the continuous covariate age. The main focus of chapter 3 was to develop a model that relaxes the stationarity assumption. This was achieved by allowing the e ects of the covariates to vary spatially by using the conditional autoregressive model. This new model performed better than the stationary models. In chapter 4 we introduce a spatial temporal spatially varying covariate model. In this model, the covariates were allowed to vary both spatially and temporally. We fit this model to the Angolan malaria data. The fifth chapter presents a review of various assumptions in spatial disease modeling and improvements for some limiting assumptions such as the normality assumption on random effects and linearity assumption on the covariates. We use the non-parametric spatial model approach to relax the limiting normality assumption. The last part of chapter 5 involves developing a joint spatially varying model (an extension of the spatially varying coefficient model in chapter 3) and fitting it to the HIV and HSV-2 data. Chapter six of the study provides the overview of the thesis, the conclusion and presents areas of further studies.
Bayesian spatial models with application to HIV, TB and STI modeling in Kenya.
(2014) Owino, Ngesa Oscar.; Mwambi, Henry Godwell.; Achia, Thomas Noel Ochieng.
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.