Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022

Samuel Okinyi; Joseph Njuguna Karomo; Nzioka Dorcas Mbinya; Mohammed Kassim Thoya

doi:doi:10.11648/j.ijdsa.20241002.12

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Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022

Samuel Okinyi, Joseph Njuguna Karomo^*, Nzioka Dorcas Mbinya, Mohammed Kassim Thoya

Published in International Journal of Data Science and Analysis (Volume 10, Issue 2)

Received: 14 May 2024 Accepted: 6 June 2024 Published: 19 June 2024

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Abstract

Cholera is a deadly disease caused by consumption of either food or water that is contaminated with bacterium known as Vibro cholerae. In the 19^th century, Cholera had spread across the globe from its original source in Ganges Delta, India. This research aimed to investigated the factors contributing to the spread of Cholera in Kenya from 2000 to 2022. The key objective was to fit a multiple linear regression model which aid in determining the goodness of fit as well as examining the relationship between different types of drinking water and spread of Cholera. Secondary data was obtained from United Nations International Children’s Emergency Fund (UNICEF) and World Health Organization (WHO). The results showed a strong positive linear relationship between Surface water and proportion of population affected by Cholera. 88% total variation in the proportion of population being affected by Cholera can be explained by the Unimproved Basic water (UBW), Surface water (SW) and Least Basic drinking water (LBW). Significantly this research encouraged the use of the least basic drinking water which clearly showed an inverse proportion to the proportion of population affected by Cholera. In addition to that, the researchers recommend improved access to clean water and create awareness on the dangers of using untreated water.

Published in	International Journal of Data Science and Analysis (Volume 10, Issue 2)
DOI	10.11648/j.ijdsa.20241002.12
Page(s)	33-40
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

Cholera, Multiple Linear Regression, Coefficient of Determination, Correlation Coefficient

References

[1]	Almagro-Moreno, S., & Taylor, R. K. (2013). Cholera: environmental reservoirs and impact on disease transmission. Microbiology spectrum, 1(2), 10-1128. https://doi.org/10.1128/microbiolspec.oh-0003-2012
[2]	Ayinde, K., Apata, E. O., & Alaba, O. O. (2012). Estimators of linear regression model and prediction under some assumptions violation. https://doi.org/10.4236/ojs.2012.25069
[3]	Charnley, G. E., Kelman, I., & Murray, K. A. (2022). Drought-related cholera outbreaks in Africa and the implications for climate change: a narrative review. Pathogens and global health, 116(1), 3-12. https://doi.org/10.1080/20477724.2021.1981716
[4]	Cowman, G., Otipo, S., Njeru, I., Achia, T., Thirumurthy, H., Bartram, J., & Kioko, J. (2017). Factors associated with cholera in Kenya, 2008- 2013. Pan African Medical Journal, 28(1), 156-156. https://doi.org/10.11604/pamj.2017.28.101.12806
[5]	Gaffney, M. (2016). Nature, economy, and equity: Sacred water, profane markets. American Journal of Economics and Sociology, 75(5), 1064-1231. https://doi.org/10.1111/ajes.12169
[6]	Girotto, C. D., Behzadian, K., Musah, A., Chen, A. S., Ali, A., & Campos, L. C. (2021). Impact of water and sanitation services on cholera outbreaks in sub-Saharan Africa.
[7]	Hickey, G. L. Checking model assumptions with regression diagnostics.
[8]	Leidner, A. J., & Adusumilli, N. C. (2013). Estimating effects of improved drinking water and sanitation on cholera. Journal of water and health, 11(4), 671-683. https://doi.org/10.2166/wh.2013.238
[9]	Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
[10]	Mutonga, D., Langat, D., Mwangi, D., Tonui, J., Njeru, M., Abade, A., ... & Dahlke, M. (2013). National surveillance data on the epidemiology of cholera in Kenya, 1997-2010. The Journal of infectious diseases, 208 (suppl-1), S55-S61. https://doi.org/10.1093/infdis/jit201
[11]	Ntajal, J., Falkenberg, T., Kistemann, T., & Evers, M. (2020). Influences of land-use dynamics and surface water systems interactions on water-related infectious diseases? A systematic review. Water, 12(3), 631. https://doi.org/10.3390/w12030631
[12]	Ogisma, L., Li, T., Xiao, H., O’Donnell, F., & Molnar, J. (2021). Analysis of community level factors contributing to cholera infection and water testing access in the NorthernCorridorofHaiti. WaterEnvironmentResearch, 93(10), 1819-1828. https://doi.org/10.1002/wer.1591
[13]	Oyugi, E. O., Boru, W., Obonyo, M., Githuku, J., Onyango, D., Wandeba, A., ... & Gura, Z. (2017). An outbreak of cholera in western Kenya, 2015: a case control study. The Pan African Medical Journal, 28 (Suppl 1). https://doi.org/10.11604/pamj.supp.2017.28.1.9477
[14]	Rebaudet, S., Sudre, B., Faucher, B., & Piarroux, R. (2013). Environmental determinants of cholera outbreaks in inland Africa: a systematic review of main transmission foci and propagation routes. The Journal of infectious diseases, 208 (suppl-1), S46-S54. https://doi.org/10.1093/infdis/jit195
[15]	Shapiro, R. L., Otieno, M. R., Adcock, P. M., Phillips- Howard, P. A., Hawley, W. A., Kumar, L., ... & Slutsker, L. (1999). Transmission of epidemic Vibrio cholerae O1 in rural western Kenya associated with drinking water from Lake Victoria: an environmental reservoir for cholera? The American journal of tropical medicine and hygiene, 60(2), 271-276.
[16]	Shultz, A., Omollo, J. O., Burke, H., Qassim, M., Ochieng, J. B., Weinberg, M., ... & Breiman, R. F. (2009). Cholera outbreak in Kenyan refugee camp: risk factors for illness and importance of sanitation. American Journal of Tropical Medicine and Hygiene, 80(4), 640.
[17]	Sulayman, F. A. T. I. M. A. (2014). Modelling and analysis of the spread of cholera disease in Nigeria with environmental control. Research thesis.

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APA Style

Okinyi, S., Karomo, J. N., Mbinya, N. D., Thoya, M. K. (2024). Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022. International Journal of Data Science and Analysis, 10(2), 33-40. https://doi.org/10.11648/j.ijdsa.20241002.12

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ACS Style

Okinyi, S.; Karomo, J. N.; Mbinya, N. D.; Thoya, M. K. Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022. Int. J. Data Sci. Anal. 2024, 10(2), 33-40. doi: 10.11648/j.ijdsa.20241002.12

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AMA Style

Okinyi S, Karomo JN, Mbinya ND, Thoya MK. Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022. Int J Data Sci Anal. 2024;10(2):33-40. doi: 10.11648/j.ijdsa.20241002.12

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@article{10.11648/j.ijdsa.20241002.12,
  author = {Samuel Okinyi and Joseph Njuguna Karomo and Nzioka Dorcas Mbinya and Mohammed Kassim Thoya},
  title = {Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022},
  journal = {International Journal of Data Science and Analysis},
  volume = {10},
  number = {2},
  pages = {33-40},
  doi = {10.11648/j.ijdsa.20241002.12},
  url = {https://doi.org/10.11648/j.ijdsa.20241002.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241002.12},
  abstract = {Cholera is a deadly disease caused by consumption of either food or water that is contaminated with bacterium known as Vibro cholerae. In the 19th century, Cholera had spread across the globe from its original source in Ganges Delta, India. This research aimed to investigated the factors contributing to the spread of Cholera in Kenya from 2000 to 2022. The key objective was to fit a multiple linear regression model which aid in determining the goodness of fit as well as examining the relationship between different types of drinking water and spread of Cholera. Secondary data was obtained from United Nations International Children’s Emergency Fund (UNICEF) and World Health Organization (WHO). The results showed a strong positive linear relationship between Surface water and proportion of population affected by Cholera. 88% total variation in the proportion of population being affected by Cholera can be explained by the Unimproved Basic water (UBW), Surface water (SW) and Least Basic drinking water (LBW). Significantly this research encouraged the use of the least basic drinking water which clearly showed an inverse proportion to the proportion of population affected by Cholera. In addition to that, the researchers recommend improved access to clean water and create awareness on the dangers of using untreated water.},
 year = {2024}
}

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TY  - JOUR
T1  - Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022
AU  - Samuel Okinyi
AU  - Joseph Njuguna Karomo
AU  - Nzioka Dorcas Mbinya
AU  - Mohammed Kassim Thoya
Y1  - 2024/06/19
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdsa.20241002.12
DO  - 10.11648/j.ijdsa.20241002.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 33
EP  - 40
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20241002.12
AB  - Cholera is a deadly disease caused by consumption of either food or water that is contaminated with bacterium known as Vibro cholerae. In the 19th century, Cholera had spread across the globe from its original source in Ganges Delta, India. This research aimed to investigated the factors contributing to the spread of Cholera in Kenya from 2000 to 2022. The key objective was to fit a multiple linear regression model which aid in determining the goodness of fit as well as examining the relationship between different types of drinking water and spread of Cholera. Secondary data was obtained from United Nations International Children’s Emergency Fund (UNICEF) and World Health Organization (WHO). The results showed a strong positive linear relationship between Surface water and proportion of population affected by Cholera. 88% total variation in the proportion of population being affected by Cholera can be explained by the Unimproved Basic water (UBW), Surface water (SW) and Least Basic drinking water (LBW). Significantly this research encouraged the use of the least basic drinking water which clearly showed an inverse proportion to the proportion of population affected by Cholera. In addition to that, the researchers recommend improved access to clean water and create awareness on the dangers of using untreated water.
VL  - 10
IS  - 2
ER  -

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Author Information

Samuel Okinyi

Department of Pure and Applied Sciences, Kirinyaga University, Nairobi, Kenya

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Joseph Njuguna Karomo

Department of Pure and Applied Sciences, Kirinyaga University, Nairobi, Kenya

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Nzioka Dorcas Mbinya

Department of Pure and Applied Sciences, Kirinyaga University, Nairobi, Kenya

Contact Email
Mohammed Kassim Thoya

Department of Pure and Applied Sciences, Kirinyaga University, Nairobi, Kenya

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Plain Text BibTeX RIS

APA Style

Okinyi, S., Karomo, J. N., Mbinya, N. D., Thoya, M. K. (2024). Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022. International Journal of Data Science and Analysis, 10(2), 33-40. https://doi.org/10.11648/j.ijdsa.20241002.12

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ACS Style

Okinyi, S.; Karomo, J. N.; Mbinya, N. D.; Thoya, M. K. Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022. Int. J. Data Sci. Anal. 2024, 10(2), 33-40. doi: 10.11648/j.ijdsa.20241002.12

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AMA Style

Okinyi S, Karomo JN, Mbinya ND, Thoya MK. Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022. Int J Data Sci Anal. 2024;10(2):33-40. doi: 10.11648/j.ijdsa.20241002.12

Copy | Download

@article{10.11648/j.ijdsa.20241002.12,
  author = {Samuel Okinyi and Joseph Njuguna Karomo and Nzioka Dorcas Mbinya and Mohammed Kassim Thoya},
  title = {Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022},
  journal = {International Journal of Data Science and Analysis},
  volume = {10},
  number = {2},
  pages = {33-40},
  doi = {10.11648/j.ijdsa.20241002.12},
  url = {https://doi.org/10.11648/j.ijdsa.20241002.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241002.12},
  abstract = {Cholera is a deadly disease caused by consumption of either food or water that is contaminated with bacterium known as Vibro cholerae. In the 19th century, Cholera had spread across the globe from its original source in Ganges Delta, India. This research aimed to investigated the factors contributing to the spread of Cholera in Kenya from 2000 to 2022. The key objective was to fit a multiple linear regression model which aid in determining the goodness of fit as well as examining the relationship between different types of drinking water and spread of Cholera. Secondary data was obtained from United Nations International Children’s Emergency Fund (UNICEF) and World Health Organization (WHO). The results showed a strong positive linear relationship between Surface water and proportion of population affected by Cholera. 88% total variation in the proportion of population being affected by Cholera can be explained by the Unimproved Basic water (UBW), Surface water (SW) and Least Basic drinking water (LBW). Significantly this research encouraged the use of the least basic drinking water which clearly showed an inverse proportion to the proportion of population affected by Cholera. In addition to that, the researchers recommend improved access to clean water and create awareness on the dangers of using untreated water.},
 year = {2024}
}

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TY  - JOUR
T1  - Multiple Linear Regression Analysis of Factors Contributing to the Spread of Cholera in Kenya from 2000 to 2022
AU  - Samuel Okinyi
AU  - Joseph Njuguna Karomo
AU  - Nzioka Dorcas Mbinya
AU  - Mohammed Kassim Thoya
Y1  - 2024/06/19
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdsa.20241002.12
DO  - 10.11648/j.ijdsa.20241002.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 33
EP  - 40
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20241002.12
AB  - Cholera is a deadly disease caused by consumption of either food or water that is contaminated with bacterium known as Vibro cholerae. In the 19th century, Cholera had spread across the globe from its original source in Ganges Delta, India. This research aimed to investigated the factors contributing to the spread of Cholera in Kenya from 2000 to 2022. The key objective was to fit a multiple linear regression model which aid in determining the goodness of fit as well as examining the relationship between different types of drinking water and spread of Cholera. Secondary data was obtained from United Nations International Children’s Emergency Fund (UNICEF) and World Health Organization (WHO). The results showed a strong positive linear relationship between Surface water and proportion of population affected by Cholera. 88% total variation in the proportion of population being affected by Cholera can be explained by the Unimproved Basic water (UBW), Surface water (SW) and Least Basic drinking water (LBW). Significantly this research encouraged the use of the least basic drinking water which clearly showed an inverse proportion to the proportion of population affected by Cholera. In addition to that, the researchers recommend improved access to clean water and create awareness on the dangers of using untreated water.
VL  - 10
IS  - 2
ER  -

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