• Login
    View Item 
    •   Mak IR Home
    • College of Computing and Information Sciences (CoCIS)
    • School of Computing and Informatics Technology (CIT)
    • School of Computing and Informatics Technology (CIT) Collection
    • View Item
    •   Mak IR Home
    • College of Computing and Information Sciences (CoCIS)
    • School of Computing and Informatics Technology (CIT)
    • School of Computing and Informatics Technology (CIT) Collection
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A model for spatial variability of typhoid disease incidences in Uganda

    Thumbnail
    View/Open
    PhD thesis (5.694Mb)
    Date
    2022-04
    Author
    Kamukama, Ismail
    Metadata
    Show full item record
    Abstract
    Environmental and demographic factors are responsible for occurrences of typhoid disease most especially in developing countries of Africa and Asia. Existing studies show that these risk factors differ at global, regional, national and sub-national levels. Uganda is one of the developing countries with high incidences of typhoid disease. However, spatial variability of the disease has not been explored and accounted for on local scale, and this makes surveillance inefficient and expensive. Therefore, the aim of the study was to account for spatial variability of typhoid disease incidences in Uganda, using data science method. In the process, spatial-temporal trends, distribution patterns and factors responsible for spatial variability of typhoid disease were first determined before developing the model. Spatial-temporal trends revealed an increasing trend of the disease nationally. Discrete Poisson’s model was used to explore spatial-temporal patterns and revealed most of the disease clustering in central region, followed by Western and Eastern regions. Northern region was the safest region throughout the study period of 2012 to 2017. A Spatial Error model revealed that poor handwashing practice, rainfall and poor drainage (floods effect) were responsible for spatial variability of typhoid disease incidences in Uganda. The Geographically Weighted Regression model revealed that poor handwashing practice mainly influenced typhoid disease occurrences in Northwestern, Northern and Northeastern parts of the country. High rainfall was most responsible for disease incidences in the Eastern, Central and Southern parts of the country. Poor drainage was mainly influencing disease in the Western, Central and Southern parts of the country. On evaluation, the Geographically Weighted Regression model performed better than the global regression model. With out-of-sample data, the model was able to identify high and low-risk areas. The model was further evaluated by a survey involving planners and decision makers in the MOH. The results of the survey revealed ease of use, usefulness and possible chances of model adoption. This knowledge is essential for planners and decision-makers to: efficiently plan, enforce preventive measures and make targeted interventions. Targeted interventions support resource optimization which eventually reduces surveillance costs.
    URI
    http://hdl.handle.net/10570/10118
    Collections
    • School of Computing and Informatics Technology (CIT) Collection

    DSpace 5.8 copyright © Makerere University 
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of Mak IRCommunities & CollectionsTitlesAuthorsBy AdvisorBy Issue DateSubjectsBy TypeThis CollectionTitlesAuthorsBy AdvisorBy Issue DateSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace 5.8 copyright © Makerere University 
    Contact Us | Send Feedback
    Theme by 
    Atmire NV