• Login
    View Item 
    •   ResearchSpace Home
    • College of Agriculture, Engineering and Science
    • School Mathematics, Statistics and Computer Science
    • Statistics
    • Masters Degrees (Statistics)
    • View Item
    •   ResearchSpace Home
    • College of Agriculture, Engineering and Science
    • School Mathematics, Statistics and Computer Science
    • Statistics
    • Masters Degrees (Statistics)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Bayesian hierarchical spatial and spatio-temporal modeling and mapping of tuberculosis in Kenya.

    Thumbnail
    View/Open
    Thesis. (4.836Mb)
    Date
    2013
    Author
    Iddrisu, Abdul-Karim.
    Metadata
    Show full item record
    Abstract
    Global spread of infectious disease threatens the well-being of human, domestic, and wildlife health. A proper understanding of global distribution of these diseases is an important part of disease management and policy making. However, data are subject to complexities by heterogeneity across host classes and space-time epidemic processes [Waller et al., 1997, Hosseini et al., 2006]. The use of frequentist methods in Biostatistics and epidemiology are common and are therefore extensively utilized in answering varied research questions. In this thesis, we proposed the Hierarchical Bayesian approach to study the spatial and the spatio-temporal pattern of tuberculosis in Kenya [Knorr-Held et al., 1998, Knorr-Held, 1999, L opez-Qu lez and Munoz, 2009, Waller et al., 1997, Julian Besag, 1991]. Space and time interaction of risk (ψ[ij]) is an important factor considered in this thesis. The Markov Chain Monte Carlo (MCMC) method via WinBUGS and R packages were used for simulations [Ntzoufras, 2011, Congdon, 2010, David et al., 1995, Gimenez et al., 2009, Brian, 2003], and the Deviance Information Criterion (DIC), proposed by [Spiegelhalter et al., 2002], used for models comparison and selection. Variation in TB risk is observed among Kenya counties and clustering among counties with high TB relative risk (RR). HIV prevalence is identified as the dominant determinant of TB. We found clustering and heterogeneity of risk among high rate counties and the overall TB risk is slightly decreasing from 2002-2009. Interaction of TB relative risk in space and time is found to be increasing among rural counties that share boundaries with urban counties with high TB risk. This is as a result of the ability of models to borrow strength from neighbouring counties, such that near by counties have similar risk. Although the approaches are less than ideal, we hope that our formulations provide a useful stepping stone in the development of spatial and spatio-temporal methodology for the statistical analysis of risk from TB in Kenya.
    URI
    http://hdl.handle.net/10413/10279
    Collections
    • Masters Degrees (Statistics) [87]

    DSpace software copyright © 2002-2013  Duraspace
    Contact Us | Send Feedback
    Theme by 
    @mire NV
     

     

    Browse

    All of ResearchSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsAdvisorsTypeThis CollectionBy Issue DateAuthorsTitlesSubjectsAdvisorsType

    My Account

    LoginRegister

    DSpace software copyright © 2002-2013  Duraspace
    Contact Us | Send Feedback
    Theme by 
    @mire NV