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

Kamukama Ismail; Maiga Gilbert; Ssebuggwaawo Denis; Nabende Peter; Ali Mansourian

doi:doi:10.11648/j.hep.20210602.14

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

Kamukama Ismail, Maiga Gilbert, Ssebuggwaawo Denis, Nabende Peter, Ali Mansourian

Published in International Journal of Health Economics and Policy (Volume 6, Issue 2)

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), 2021. Published by Science Publishing Group

Keywords

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

References

[1]	WHO. (2019). Immunization, Vaccines and Biologicals, Typhoid, https://www.who.int/immunization/diseases/typhoid/en/. (Accessed on 22/08/2020).
[2]	Kabwama, S., N., Bulage, L., Nsubuga, F. et al. (2017). A large and persistent outbreak of typhoid fever caused by consuming contaminated water and street-vended beverages: Kampala, Uganda, January – June 2015. BMC Public Health 17, 23 (2017) doi: 10.1186/s12889-016-4002-0.
[3]	Crump, J., A., (2017). Progress in Typhoid Fever Epidemiology, Supplementary articles, Clinical Infectious Diseases, 2019; 68 (S1): S4–9, Centre for International Health, University of Otago, Dunedin, New Zealand.
[4]	Mirembe, B., B., Mazeri, S., Callaby, R., Nyakarahuka, L., Kankya, C., Muwonge, A. (2019). Temporal, spatial and household dynamics of Typhoid fever in Kasese district, Uganda. PLoS ONE 14 (4): e0214650.https://doi.org/10.1371/journal.pone.0214650.
[5]	Greenwell, J., et al. (2013). Typhoid fever: hurdles to adequate hand washing for disease prevention among the population of a peri-urban informal settlement in Fiji. Western Pacific Surveillance and Response Journal, 2013, 4 (1): 41–45. doi: 10.5365/wpsar.2012.3.4.006.
[6]	Akullian, A., Ng’eno, E., Matheson, AI, Cosmas, L., Macharia, D., Fields, B., et al. (2015). Environmental Transmission of Typhoid Fever in an Urban Slum. PLoS Negl Trop Dis 9 (12): e0004212. https://doi.org/10.1371/journal.pntd.0004212.
[7]	Breiman, R., F., Cosmas, L., Njuguna, H., Audi, A., Olack, B., Ochieng, J., B.,… Feikin, D., R. (2012). Population-based incidence of typhoid fever in an urban informal settlement and a rural area in Kenya: implications for typhoid vaccine use in Africa. PloS one, 7 (1), e29119. doi: 10.1371/journal.pone.0029119.
[8]	Jenkins, A., P., Jupiter, S., D., Jenney, A., Rosa, V., Naucukidi, A., Prasad, N., Vosaki, G., Mulholland, K., Strugnell, R., Kama, M., Crump, J., A., Horwitz, P. (2019). Environmental Foundations of Typhoid Fever in the Fijian Residential Setting, International Journal of Environmental Research and Public Health.
[9]	Dewan, A., M., Corner, R., Hashizume, M., Ongee, E., T. (2013). Typhoid Fever and its association with environmental factors in the Dhaka Metropolitan Area of Bangladesh: a spatial and time-series approach. PLoS neglected tropical diseases, 7 (1), e1998. doi: 10.1371/journal.pntd.0001998.
[10]	Kim, O., S., Nugent, J., B., Y, Newell, J., P., Curtis, A., J. (2017). A Mixed Application of Geographically Weighted Regression and Unsupervised Classification for Analyzing Latex Yield Variability in Yunnan, China. Forests, 8 (5), 162. https://doi.org/10.3390/f8050162.
[11]	Saad, N., J., Lynch, V., D., Antillón, M. et al. (2018). Seasonal dynamics of typhoid and paratyphoid fever. Sci Rep 8, 6870 (2018), doi: 10.1038/s41598-018-25234-w.
[12]	Kala et al. (2017). A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters. PeerJ 5: e3070; DOI 10.7717/peerj.3070.
[13]	Oshan, T., M., Fotheringham, A., S. (2018). A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial-Filter-Based Techniques, Geographical Analysis (2018) 50, 53–75.
[14]	Charlton, M., Fotheringham, A., S., (2019). Geographically Weighted Regression, A Tutorial on using GWR in ArcGIS 9.3, National Centre for Geocomputation, National University of Ireland Maynooth. https://www.geos.ed.ac.uk/~gisteac/fspat/gwr/gwr_arcgis/GWR_Tutorial.pdf.
[15]	Anselin, L. (2005). Exploring Spatial Data with GeoDa: Workbook.
[16]	UBOS, 2012. Statistical Abstract 2012, Uganda Bureau of Statistics, http://www.ubos.org/onlinefiles/uploads/ubos/pdf%20documents/2012StatisticalAbstract.pdf.
[17]	Nahimana, M., R., Candide, T., N., Olushayo, O., Nyamusore, J., Isiaka, A., Ndahindwa, V., Dassanayake, L., Rusanganwa, A. (2017). Knowledge, attitude and practice of hygiene and sanitation in a Burundian refugee camp: implications for control of a Salmonella typhi outbreak, Pan African Medical Journal, http://www.panafrican-med-journal.com/content/article/28/54/full.
[18]	Brainard, J., D’hondt, R., Ali, E., Van denBergh, R., De Weggheleire, A., Baudot, Y., et al. (2018). Typhoid fever outbreak in the Democratic Republic of Congo: Case control and ecological study. PLoSNegl Trop Dis 12 (10): e0006795. https://doi.org/10.1371/journal.pntd.0006795.
[19]	Reyes, R., Ahn, R., Thurber, K., Burke, T., F. (2013). Urbanization and Infectious Diseases: General Principles, Historical Perspectives, and Contemporary Challenges. In: Fong I. (eds) Challenges in Infectious Diseases. Emerging Infectious Diseases of the 21st Century. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4496-1_4.
[20]	Mourad, K., A., Habomugisha, V., Sule, B., F. (2019). Assessing Students’ Knowledge on WASH-Related Diseases, Int. J. Environ. Res. Public Health 2019, 16 (11), 2052; https://doi.org/10.3390/ijerph16112052.
[21]	Sur, D., Ali, M., von Seidlein, L., et al. (2007). Comparisons of predictors for typhoid and paratyphoid fever in Kolkata, India. BMC Public Health 7, 289 (2007). https://doi.org/10.1186/1471-2458-7-289.
[22]	Ozturk, D., & kilic, F. (2016). Geostatistical Approach for Spatial Interpolation of Meteorological, Anais da Academia Brasileira de Ciências (2016) 88 (4): 2121-2136.
[23]	Shyti, B., Valera, D. (2018). The Regression Model for the Statistical Analysis of Albanian Economy, Department of Mathematics, FNS, University of Elbasan Albania, International Journal of Mathematics Trends and Technology (IJMTT) – Volume 62 Number 2 – October2018.
[24]	Cheng, W., Jian, Z., Y., (2018). Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmospheric Measurement Techniques, EGU. https://doi.org/10.5194/amt-11-1233-2018.
[25]	Comber, A., Wang, Y., Lü, Y., Zhang, X., Harris, P. (2018). Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization. Journal of Spatial Information Science, Number 17 (2018), pp. 63–84, doi: 10.5311/JOSIS.2018.17.422.
[26]	Goschin, Z. (2018). "Regional patterns of Romanian emigration. A Geographically Weighted Regression Model," Romanian Journal of Economics, Institute of National Economy, vol. 46 (1 (55)), pages 60-74, June.
[27]	Xiao, D., Xu, X., Duan, L. (2019). Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model, Hindawi Journal of Advanced Transportation Volume 2019, Article ID 8521649, 15 pages, https://doi.org/10.1155/2019/8521649.
[28]	Hirai, M., Roess, A., Huang, C., Graham, J. (2016). Exploring geographic distributions of high-risk water, sanitation, and hygiene practices and their association with child diarrhea in Uganda. Glob Health Action, 2016; 9: 10.3402/gha.v9.32833, doi: 10.3402/gha.v9.32833.
[29]	Stanton, M., C., Adriko, M., Arinaitwe, M., Howel, A., Davies, J., Allison, G., E., LaCourse, J., Muheki, E., Kabatereine, N., B., Stothard, J., L. (2017). Intestinal schistosomiasis in Uganda at high altitude (>1400m): Malacological and epidemiological surveys on Mount Elgon and in Fort Portal crater lakes reveal extra preventive chemotherapy needs, Infectious Diseases of Poverty (2017) 6: 34 DOI 10.1186/s40249-017-0248-8.
[30]	Akankwasa, B. (2018). Uganda Red Cross supporting communities affected by cholera, floods and landslides in Western Uganda, https://www.unicef.org/uganda/stories/uganda-red-cross-supports-communities-affected-cholera-floods-and-landslides-western-uganda. (Accessed 25/03/2020).
[31]	Atuyambe, L., M., Ediau, M., Orach, C., G., Musenero, M., Bazeyo, W. (2011). Land slide disaster in eastern Uganda: rapid assessment of water, sanitation and hygiene situation in Bulucheke camp, Bududa district, Environmental health: a global access science source, 10, 38. doi: 10.1186/1476-069X-10-38.
[32]	Garcia, J., & Markandya, A. (2015). Economic Assessment of the Impacts of Climate Change in Uganda, Kampala Case-Study, Ministry of Water and Environment, https://cdkn.org/wp-content/uploads/2015/12/Uganda_CC-Economics_Kampala_Case-study2.pdf.
[33]	NEMA. (2016). State of the Environment Report for Uganda 2014. National Environment Management Authority (NEMA), Kampala.
[34]	Liu, Z., Lao, J., Zhang, Y., Liu, Y., Zhang, J., Wang, H., Jiang, B. (2018). Association between floods and typhoid fever in Yongzhou, China: Effects and vulnerable groups, Environmental Research Volume 167, November 2018, Pages 718-724, https://doi.org/10.1016/j.envres.2018.08.030.

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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
AU  - Nabende Peter
AU  - Ali Mansourian
Y1  - 2021/05/08
PY  - 2021
N1  - https://doi.org/10.11648/j.hep.20210602.14
DO  - 10.11648/j.hep.20210602.14
T2  - International Journal of Health Economics and Policy
JF  - International Journal of Health Economics and Policy
JO  - International Journal of Health Economics and Policy
SP  - 56
EP  - 64
PB  - Science Publishing Group
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

Kamukama Ismail

Department of Information Systems, Makerere University, Kampala, Uganda
Maiga Gilbert

Department of Information Systems, Makerere University, Kampala, Uganda
Ssebuggwaawo Denis

Department of Computer Science, Kyambogo University, Kampala, Uganda
Nabende Peter

Department of Information Systems, Makerere University, Kampala, Uganda
Ali Mansourian

Department of Physical Geography, Lund University, Lund, Sweden

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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

Copy | Download

@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}
}

Copy | Download

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
AU  - Nabende Peter
AU  - Ali Mansourian
Y1  - 2021/05/08
PY  - 2021
N1  - https://doi.org/10.11648/j.hep.20210602.14
DO  - 10.11648/j.hep.20210602.14
T2  - International Journal of Health Economics and Policy
JF  - International Journal of Health Economics and Policy
JO  - International Journal of Health Economics and Policy
SP  - 56
EP  - 64
PB  - Science Publishing Group
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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