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

Safari Godfrey Lyece; Samuel Mwalili; Joseph Kyalo Mung’atu

doi:doi:10.11648/j.ajtas.20231203.11

| Peer-Reviewed

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

Safari Godfrey Lyece, Samuel Mwalili, Joseph Kyalo Mung’atu

Published in American Journal of Theoretical and Applied Statistics (Volume 12, Issue 3)

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. 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

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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
Y1  - 2023/05/24
PY  - 2023
N1  - https://doi.org/10.11648/j.ajtas.20231203.11
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
SP  - 37
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  -

Copy | Download

Author Information

Safari Godfrey Lyece

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

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Joseph Kyalo Mung’atu

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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
Y1  - 2023/05/24
PY  - 2023
N1  - https://doi.org/10.11648/j.ajtas.20231203.11
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
SP  - 37
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  -

Copy | Download