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Spatial Variability of Typhoid Disease Incidences in Uganda Using Geographically Weighted Regression Approach

Received: 8 April 2021    Accepted: 21 April 2021    Published: 8 May 2021
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Abstract

The spatial variability of typhoid disease incidences has not been accounted for, most especially in developing countries, which makes its surveillance inefficient and expensive. This research aimed at (i) exploring possible risk factors of typhoid disease and (ii) accounting for spatial variability of typhoid disease incidences using GWR approach. The research first explored possible risk factors of typhoid disease using global regression-Ordinary Least Squares (OLS). Geographically Weighted Regression (GWR) model was used to account for spatial variability of typhoid disease incidences. Moran’s Index was used to confirm spatial patterns in the data. The research revealed that; poor handwashing practice, rainfall and poor drainage (floods effect) were responsible for spatial variability of typhoid disease locally (P<0.05). GWR model revealed that poor handwashing practice was mainly responsible for typhoid disease occurrences in Northern, Northwestern and Mid-central parts of the country while excessive rainfall was mainly responsible for occurrence of the disease in the Eastern and Western regions. Poor drainage was mainly influencing disease occurrences in Eastern and Southwestern parts of the country. In the analysis, GWR model performed better than global OLS model (R-squared=0.37, R-squared=0.25 respectively). A combination of poor handwashing practice, excessive rainfall and poor drainage accounts for spatial variability of typhoid disease incidences in Uganda. This knowledge is very essential for surveillance teams to (i) enforce preventive measures, (ii) prepare for outbreaks and (iii) make targeted interventions to eventually reduce operational costs.

Published in International Journal of Health Economics and Policy (Volume 6, Issue 2)
DOI 10.11648/j.hep.20210602.14
Page(s) 56-64
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Geographically Weighted Regression (GWR), Global Regression, Ordinary Least Squares (OLS), Typhoid, Uganda

References
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Cite This Article
  • APA Style

    Kamukama Ismail, Maiga Gilbert, Ssebuggwaawo Denis, Nabende Peter, Ali Mansourian. (2021). Spatial Variability of Typhoid Disease Incidences in Uganda Using Geographically Weighted Regression Approach. International Journal of Health Economics and Policy, 6(2), 56-64. https://doi.org/10.11648/j.hep.20210602.14

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    ACS Style

    Kamukama Ismail; Maiga Gilbert; Ssebuggwaawo Denis; Nabende Peter; Ali Mansourian. Spatial Variability of Typhoid Disease Incidences in Uganda Using Geographically Weighted Regression Approach. Int. J. Health Econ. Policy 2021, 6(2), 56-64. doi: 10.11648/j.hep.20210602.14

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    AMA Style

    Kamukama Ismail, Maiga Gilbert, Ssebuggwaawo Denis, Nabende Peter, Ali Mansourian. Spatial Variability of Typhoid Disease Incidences in Uganda Using Geographically Weighted Regression Approach. Int J Health Econ Policy. 2021;6(2):56-64. doi: 10.11648/j.hep.20210602.14

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  • @article{10.11648/j.hep.20210602.14,
      author = {Kamukama Ismail and Maiga Gilbert and Ssebuggwaawo Denis and Nabende Peter and Ali Mansourian},
      title = {Spatial Variability of Typhoid Disease Incidences in Uganda Using Geographically Weighted Regression Approach},
      journal = {International Journal of Health Economics and Policy},
      volume = {6},
      number = {2},
      pages = {56-64},
      doi = {10.11648/j.hep.20210602.14},
      url = {https://doi.org/10.11648/j.hep.20210602.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hep.20210602.14},
      abstract = {The spatial variability of typhoid disease incidences has not been accounted for, most especially in developing countries, which makes its surveillance inefficient and expensive. This research aimed at (i) exploring possible risk factors of typhoid disease and (ii) accounting for spatial variability of typhoid disease incidences using GWR approach. The research first explored possible risk factors of typhoid disease using global regression-Ordinary Least Squares (OLS). Geographically Weighted Regression (GWR) model was used to account for spatial variability of typhoid disease incidences. Moran’s Index was used to confirm spatial patterns in the data. The research revealed that; poor handwashing practice, rainfall and poor drainage (floods effect) were responsible for spatial variability of typhoid disease locally (P<0.05). GWR model revealed that poor handwashing practice was mainly responsible for typhoid disease occurrences in Northern, Northwestern and Mid-central parts of the country while excessive rainfall was mainly responsible for occurrence of the disease in the Eastern and Western regions. Poor drainage was mainly influencing disease occurrences in Eastern and Southwestern parts of the country. In the analysis, GWR model performed better than global OLS model (R-squared=0.37, R-squared=0.25 respectively). A combination of poor handwashing practice, excessive rainfall and poor drainage accounts for spatial variability of typhoid disease incidences in Uganda. This knowledge is very essential for surveillance teams to (i) enforce preventive measures, (ii) prepare for outbreaks and (iii) make targeted interventions to eventually reduce operational costs.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Spatial Variability of Typhoid Disease Incidences in Uganda Using Geographically Weighted Regression Approach
    AU  - Kamukama Ismail
    AU  - Maiga Gilbert
    AU  - Ssebuggwaawo Denis
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    JF  - International Journal of Health Economics and Policy
    JO  - International Journal of Health Economics and Policy
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    SN  - 2578-9309
    UR  - https://doi.org/10.11648/j.hep.20210602.14
    AB  - The spatial variability of typhoid disease incidences has not been accounted for, most especially in developing countries, which makes its surveillance inefficient and expensive. This research aimed at (i) exploring possible risk factors of typhoid disease and (ii) accounting for spatial variability of typhoid disease incidences using GWR approach. The research first explored possible risk factors of typhoid disease using global regression-Ordinary Least Squares (OLS). Geographically Weighted Regression (GWR) model was used to account for spatial variability of typhoid disease incidences. Moran’s Index was used to confirm spatial patterns in the data. The research revealed that; poor handwashing practice, rainfall and poor drainage (floods effect) were responsible for spatial variability of typhoid disease locally (P<0.05). GWR model revealed that poor handwashing practice was mainly responsible for typhoid disease occurrences in Northern, Northwestern and Mid-central parts of the country while excessive rainfall was mainly responsible for occurrence of the disease in the Eastern and Western regions. Poor drainage was mainly influencing disease occurrences in Eastern and Southwestern parts of the country. In the analysis, GWR model performed better than global OLS model (R-squared=0.37, R-squared=0.25 respectively). A combination of poor handwashing practice, excessive rainfall and poor drainage accounts for spatial variability of typhoid disease incidences in Uganda. This knowledge is very essential for surveillance teams to (i) enforce preventive measures, (ii) prepare for outbreaks and (iii) make targeted interventions to eventually reduce operational costs.
    VL  - 6
    IS  - 2
    ER  - 

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Author Information
  • Department of Information Systems, Makerere University, Kampala, Uganda; Department of Computer Science, Kyambogo University, Kampala, Uganda

  • Department of Information Systems, Makerere University, Kampala, Uganda

  • Department of Computer Science, Kyambogo University, Kampala, Uganda

  • Department of Information Systems, Makerere University, Kampala, Uganda

  • Department of Physical Geography, Lund University, Lund, Sweden

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