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Supervised Machine Learning and Bayesian Regression Kriging with Application to COVID-19 Incidences in Sub-Saharan Africa

Received: 19 April 2023     Accepted: 15 May 2023     Published: 24 May 2023
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Abstract

Since COVID-19 invasion of the World, human life has been affected greatly. Several studies have shown a positive correlation between COVID-19 infections and pre-existing conditions such as Diabetes, Cancer, Tuberculosis, and Hypertension. In this study, we would like to determine whether demographic variables have a contribution to the spread of COVID-19 infections. We will apply a machine language method to select the demographic variables which are impactful in the spread of COVID-19 cases in Sub-Saharan Africa. Then we shall determine the nature of COVID-19 cases patterns applying the K-Nearest Neighbor (KNN) in calculating the neighborhood weights between locations/countries. The weights would then be tested for significance to conclude whether the cases patterns are either random, sparsely or clustered. We would then perform simulations to estimate the social demographic/covariates/fixed effects parameters and the random effects parameters. The Bayesian Kriging would be applied to predict Covid-19 cases based on the estimated social demographical variables coefficients/parameters and the random effects parameters in unknown/new locations in Sub Saharan Africa with a known uncertainty. The results showed that Children aged (0-14) years living with HIV AIDS, Prevalence of HIV Total (percentage of population ages 15-49) and Access to electricity (as a percentage of the population) was estimated to contribute to the increase of COVID-19 cases. Prediction of the COVID-19 cases in unknown locations showed that most of the cases were predicted in the elevated locations/areas than in the lower/flatter locations. This could mean that high elevated areas are associated with lower temperatures which increases the spread of COVID-19 cases as opposed to lower/flatter areas which are associated with higher temperatures which reduces the spread of COVID-19 cases.

Published in American Journal of Theoretical and Applied Statistics (Volume 12, Issue 3)
DOI 10.11648/j.ajtas.20231203.11
Page(s) 37-42
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), 2023. Published by Science Publishing Group

Keywords

K-Nearest Neighbor, Bayesian Kriging, COVID-19

References
[1] E. M. a. V. G. C. Cambaza, "COVID-19: Correlation between gross domestic product, number of tests, and confirmed cases in 13 African countries," Journal of Public Health and Epidemiology, vol. 13, no. 1, pp. 14-19, 2021.
[2] Y. a. z. P. a. C. T. Lin, "Association between social economic factors and COVID-19 outbreak in the 39 well developed cities of China," Frontiers in Public Health, vol. 8, p. 546637, 2020.
[3] M. a. O. K. J. a. W. W. a. A. S. a. G. O. Fatima, "Geospatial Analysis of COVID-19: Ascoping review," International Journal of Environment Research and Public Health, vol. 18, no. 5, p. 2336, 2021.
[4] M. a. S. S. a. K. A. a. O. Rahman, "Geology and Topology based VS30 map for Sylhet City of Bangladesh," Bulletin of Engineering Geology and the Environment, vol. 78, no. 5, pp. 3069-3083, 2019.
[5] E. a. W. D. J. a. W. C. Thompson, "A VS30 Map for California with Geologic and Topographic Contraints," Bulletin of Seismological society of America, vol. 104, no. 5, pp. 2313-2321, 2014.
[6] L. G. a. K. R. E. a. T. Y. a. T. Thompson Eric M and Baise, "A geostatistical approach to mapping site response spectral amplifications," Engineering geology, vol. 114, no. 3-4, pp. 330-342, 2010.
[7] A. a. B. D. a. P. C. a. T. P. Marache, "Geotechnical modelling at the city scale using statistical and geostatistical tools: The Pessac case (France)," Engineering Geology, vol. 107, no. 3-4, pp. 67-76, 2009.
[8] R. M. a. K. J. a. T. S. Pokhrel, "A kriging method of interpolation used to map liquefaction potential over alluvial ground," Engineering geology, vol. 152, no. 1, pp. 26-37, 2013.
[9] P. a. Gunter, "Why do we need and how should we implement Bayesian kriging methods," Stochastic Environmental Research and Risk Assessment, vol. 22, no. 5, pp. 621-632, 2008.
[10] H. Omre, "Bayesian kriging Merging observations and qualified guesses in kriging," Mathematical Geology, vol. 19, no. 1, pp. 25-39, 1987.
[11] H. a. H. K. B. Omre, "The Bayesian bridge between simple and universal kriging," Mathematical Geology, vol. 21, no. 7, pp. 767-786, 1989.
[12] A. a. G. H. Chakraborty, "A Bayesian model reflecting uncertainties on map resolutions with application to the study of site response variation," Geophysical Journal International, vol. 214, no. 3, pp. 2264-2276, 2018.
[13] R. a. D. L. F. a. G. C. E. a. P. R. M. a. V. P. J. De Risi, "The SAFER geodatabase for the Kathmandu valley: Bayesian kriging for data-scarce regions," Earthquake Spectra, vol. 37, no. 2, pp. 1108-1126, 2021.
[14] H. a. S. A. a. M. D. E. Cui, "Extension of spatial information, Bayesian kriging and updating of prior variogram parameters," Environmetrics, vol. 6, no. 4, pp. 373-384, 1995.
[15] D. F. a. C. V. D. Machuka-Mory, “Non-Stationary geostatistical modeling based on distance weighted statistics and distribution,” Mathematical Geosciences, pp. 31-48, 2013.
Cite This Article
  • APA Style

    Safari Godfrey Lyece, Samuel Mwalili, Joseph Kyalo Mung’atu. (2023). Supervised Machine Learning and Bayesian Regression Kriging with Application to COVID-19 Incidences in Sub-Saharan Africa. American Journal of Theoretical and Applied Statistics, 12(3), 37-42. https://doi.org/10.11648/j.ajtas.20231203.11

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    ACS Style

    Safari Godfrey Lyece; Samuel Mwalili; Joseph Kyalo Mung’atu. Supervised Machine Learning and Bayesian Regression Kriging with Application to COVID-19 Incidences in Sub-Saharan Africa. Am. J. Theor. Appl. Stat. 2023, 12(3), 37-42. doi: 10.11648/j.ajtas.20231203.11

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    AMA Style

    Safari Godfrey Lyece, Samuel Mwalili, Joseph Kyalo Mung’atu. Supervised Machine Learning and Bayesian Regression Kriging with Application to COVID-19 Incidences in Sub-Saharan Africa. Am J Theor Appl Stat. 2023;12(3):37-42. doi: 10.11648/j.ajtas.20231203.11

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  • @article{10.11648/j.ajtas.20231203.11,
      author = {Safari Godfrey Lyece and Samuel Mwalili and Joseph Kyalo Mung’atu},
      title = {Supervised Machine Learning and Bayesian Regression Kriging with Application to COVID-19 Incidences in Sub-Saharan Africa},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {12},
      number = {3},
      pages = {37-42},
      doi = {10.11648/j.ajtas.20231203.11},
      url = {https://doi.org/10.11648/j.ajtas.20231203.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20231203.11},
      abstract = {Since COVID-19 invasion of the World, human life has been affected greatly. Several studies have shown a positive correlation between COVID-19 infections and pre-existing conditions such as Diabetes, Cancer, Tuberculosis, and Hypertension. In this study, we would like to determine whether demographic variables have a contribution to the spread of COVID-19 infections. We will apply a machine language method to select the demographic variables which are impactful in the spread of COVID-19 cases in Sub-Saharan Africa. Then we shall determine the nature of COVID-19 cases patterns applying the K-Nearest Neighbor (KNN) in calculating the neighborhood weights between locations/countries. The weights would then be tested for significance to conclude whether the cases patterns are either random, sparsely or clustered. We would then perform simulations to estimate the social demographic/covariates/fixed effects parameters and the random effects parameters. The Bayesian Kriging would be applied to predict Covid-19 cases based on the estimated social demographical variables coefficients/parameters and the random effects parameters in unknown/new locations in Sub Saharan Africa with a known uncertainty. The results showed that Children aged (0-14) years living with HIV AIDS, Prevalence of HIV Total (percentage of population ages 15-49) and Access to electricity (as a percentage of the population) was estimated to contribute to the increase of COVID-19 cases. Prediction of the COVID-19 cases in unknown locations showed that most of the cases were predicted in the elevated locations/areas than in the lower/flatter locations. This could mean that high elevated areas are associated with lower temperatures which increases the spread of COVID-19 cases as opposed to lower/flatter areas which are associated with higher temperatures which reduces the spread of COVID-19 cases.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Supervised Machine Learning and Bayesian Regression Kriging with Application to COVID-19 Incidences in Sub-Saharan Africa
    AU  - Safari Godfrey Lyece
    AU  - Samuel Mwalili
    AU  - Joseph Kyalo Mung’atu
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    DO  - 10.11648/j.ajtas.20231203.11
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
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    EP  - 42
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20231203.11
    AB  - Since COVID-19 invasion of the World, human life has been affected greatly. Several studies have shown a positive correlation between COVID-19 infections and pre-existing conditions such as Diabetes, Cancer, Tuberculosis, and Hypertension. In this study, we would like to determine whether demographic variables have a contribution to the spread of COVID-19 infections. We will apply a machine language method to select the demographic variables which are impactful in the spread of COVID-19 cases in Sub-Saharan Africa. Then we shall determine the nature of COVID-19 cases patterns applying the K-Nearest Neighbor (KNN) in calculating the neighborhood weights between locations/countries. The weights would then be tested for significance to conclude whether the cases patterns are either random, sparsely or clustered. We would then perform simulations to estimate the social demographic/covariates/fixed effects parameters and the random effects parameters. The Bayesian Kriging would be applied to predict Covid-19 cases based on the estimated social demographical variables coefficients/parameters and the random effects parameters in unknown/new locations in Sub Saharan Africa with a known uncertainty. The results showed that Children aged (0-14) years living with HIV AIDS, Prevalence of HIV Total (percentage of population ages 15-49) and Access to electricity (as a percentage of the population) was estimated to contribute to the increase of COVID-19 cases. Prediction of the COVID-19 cases in unknown locations showed that most of the cases were predicted in the elevated locations/areas than in the lower/flatter locations. This could mean that high elevated areas are associated with lower temperatures which increases the spread of COVID-19 cases as opposed to lower/flatter areas which are associated with higher temperatures which reduces the spread of COVID-19 cases.
    VL  - 12
    IS  - 3
    ER  - 

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Author Information
  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

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