Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya

Cecilia Mbithe Titus; Anthony Wanjoya; Thomas Mageto

doi:doi:10.11648/j.ijdsa.20230902.13

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Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya

Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto

Published in International Journal of Data Science and Analysis (Volume 9, Issue 2)

Received: 21 September 2023 Accepted: 20 October 2023 Published: 31 October 2023

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Abstract

Effective management and control of communicable diseases are paramount for healthcare managers. Data-driven analysis plays a crucial role in understanding and curbing the spread of such diseases. While numerous epidemiological models have been developed to explain disease spread, many lack the incorporation of prior and posterior probabilities. In this research, we introduce a novel model called BKMR, designed to analyze and predict communicable disease occurrences in Machakos County. This study underscores the significance of data-driven approaches and outlines a plan to evaluate prediction accuracy through empirical analysis, with a particular focus on comparing BKMR with existing models using the R statistical software. We highlight the differences between estimated parameters and actual observations, emphasizing aspects not present in the training dataset. Our findings demonstrate that BKMR outperforms the Poisson regression model, offering greater flexibility and robustness. Moreover, it provides the ability to quantify uncertainty in model parameters, enhancing the capacity to make inferences about the real world. This research has substantial implications for healthcare management and disease control efforts in Machakos County.

Published in	International Journal of Data Science and Analysis (Volume 9, Issue 2)
DOI	10.11648/j.ijdsa.20230902.13
Page(s)	43-49
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

Communicable Diseases, Data Driven Analysis, Health-Care Managers, Epidemiological Models, Prior Probabilities, Posterior Probabilities, BKMR, Poisson Distribution

References

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[6]	Bang sheng, W., Jiang, Y., Xiaoqing, J. & He, L. (2020). Using Three Statistical Methods to Analyze the Association between Exposure to nine Compounds and Obesity in Children and Adolescents: NHANES 2005-2010. Environmental Health, 19 (94); 1-13. doi: 10.1186/s12940-020-00642-6.
[7]	Bobb, J. F., Henn, B. C., Valeri, L. & Brent, A. C. (2018). Statistical Software for Analyzing the Health Effects of Multiple Concurrent Exposures via Bayesian Kernel Machine Regression. Environmental Health, 17 (67); 1-10. doi: 10.1186/s12940-018-0413-y.
[8]	Bobb, J. F., Valeri, L., Claus, B. H., David, C. C., Roberto, O. W., Mazumdar, M., Godleski, J. J. & Brent, A. C. (2015). Biostatistics, 16 (3); 493-508. doi: 10.1093/biostatistics.kxu058.
[9]	Flavio, B. G., Livia, M. D. & Roger, W. C. S. (2022). Exact and Computationally Efficient Bayesian Inference for Generalized Markov Modulated Poisson Process. Statistics and Computing, 32 (14); doi: 10.1007/s11222-021-10074-y.
[10]	Frenoy, P., Perduca, V., German, C. S., Jean, P. A., Gianluca, S. & Francesca, R. M. (2022). Applications of Two Statistical Approaches (Bayesian Kernel Machine Regression and Principal Component Regression) to Assess Breast Cancer Risk in Association to Exposure to Mixtures of Brominated Flame Retardants. Environmental Health, 21 (27); 1-17. doi: 10.1186/s12940-022-00840-4.
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[12]	Heba, S. M. (2021). Empirical E-Bayesian Estimation for the Parameter of Poisson distribution. AIMS Mathematics, 6 (8); 8205-8220. doi: 10.3934/math.2021475.
[13]	Irvine, M. A. & Hollingsworth, D. T. (2018). Kernel-Density Estimation and Approximate Bayesian Computation for Flexible Epidemiological Model Fitting in Python. Epidemics, 25 (5); 80-88. doi: 10.1016/j.epidemics.2018.05.009.

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APA Style

Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto. (2023). Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya. International Journal of Data Science and Analysis, 9(2), 43-49. https://doi.org/10.11648/j.ijdsa.20230902.13

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ACS Style

Cecilia Mbithe Titus; Anthony Wanjoya; Thomas Mageto. Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya. Int. J. Data Sci. Anal. 2023, 9(2), 43-49. doi: 10.11648/j.ijdsa.20230902.13

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AMA Style

Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto. Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya. Int J Data Sci Anal. 2023;9(2):43-49. doi: 10.11648/j.ijdsa.20230902.13

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@article{10.11648/j.ijdsa.20230902.13,
  author = {Cecilia Mbithe Titus and Anthony Wanjoya and Thomas Mageto},
  title = {Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya},
  journal = {International Journal of Data Science and Analysis},
  volume = {9},
  number = {2},
  pages = {43-49},
  doi = {10.11648/j.ijdsa.20230902.13},
  url = {https://doi.org/10.11648/j.ijdsa.20230902.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20230902.13},
  abstract = {Effective management and control of communicable diseases are paramount for healthcare managers. Data-driven analysis plays a crucial role in understanding and curbing the spread of such diseases. While numerous epidemiological models have been developed to explain disease spread, many lack the incorporation of prior and posterior probabilities. In this research, we introduce a novel model called BKMR, designed to analyze and predict communicable disease occurrences in Machakos County. This study underscores the significance of data-driven approaches and outlines a plan to evaluate prediction accuracy through empirical analysis, with a particular focus on comparing BKMR with existing models using the R statistical software. We highlight the differences between estimated parameters and actual observations, emphasizing aspects not present in the training dataset. Our findings demonstrate that BKMR outperforms the Poisson regression model, offering greater flexibility and robustness. Moreover, it provides the ability to quantify uncertainty in model parameters, enhancing the capacity to make inferences about the real world. This research has substantial implications for healthcare management and disease control efforts in Machakos County.
},
 year = {2023}
}

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TY  - JOUR
T1  - Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya
AU  - Cecilia Mbithe Titus
AU  - Anthony Wanjoya
AU  - Thomas Mageto
Y1  - 2023/10/31
PY  - 2023
N1  - https://doi.org/10.11648/j.ijdsa.20230902.13
DO  - 10.11648/j.ijdsa.20230902.13
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  - 43
EP  - 49
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20230902.13
AB  - Effective management and control of communicable diseases are paramount for healthcare managers. Data-driven analysis plays a crucial role in understanding and curbing the spread of such diseases. While numerous epidemiological models have been developed to explain disease spread, many lack the incorporation of prior and posterior probabilities. In this research, we introduce a novel model called BKMR, designed to analyze and predict communicable disease occurrences in Machakos County. This study underscores the significance of data-driven approaches and outlines a plan to evaluate prediction accuracy through empirical analysis, with a particular focus on comparing BKMR with existing models using the R statistical software. We highlight the differences between estimated parameters and actual observations, emphasizing aspects not present in the training dataset. Our findings demonstrate that BKMR outperforms the Poisson regression model, offering greater flexibility and robustness. Moreover, it provides the ability to quantify uncertainty in model parameters, enhancing the capacity to make inferences about the real world. This research has substantial implications for healthcare management and disease control efforts in Machakos County.

VL  - 9
IS  - 2
ER  -

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Author Information

Cecilia Mbithe Titus

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Thomas Mageto

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

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Plain Text BibTeX RIS

APA Style

Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto. (2023). Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya. International Journal of Data Science and Analysis, 9(2), 43-49. https://doi.org/10.11648/j.ijdsa.20230902.13

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ACS Style

Cecilia Mbithe Titus; Anthony Wanjoya; Thomas Mageto. Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya. Int. J. Data Sci. Anal. 2023, 9(2), 43-49. doi: 10.11648/j.ijdsa.20230902.13

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AMA Style

Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto. Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya. Int J Data Sci Anal. 2023;9(2):43-49. doi: 10.11648/j.ijdsa.20230902.13

Copy | Download

@article{10.11648/j.ijdsa.20230902.13,
  author = {Cecilia Mbithe Titus and Anthony Wanjoya and Thomas Mageto},
  title = {Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya},
  journal = {International Journal of Data Science and Analysis},
  volume = {9},
  number = {2},
  pages = {43-49},
  doi = {10.11648/j.ijdsa.20230902.13},
  url = {https://doi.org/10.11648/j.ijdsa.20230902.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20230902.13},
  abstract = {Effective management and control of communicable diseases are paramount for healthcare managers. Data-driven analysis plays a crucial role in understanding and curbing the spread of such diseases. While numerous epidemiological models have been developed to explain disease spread, many lack the incorporation of prior and posterior probabilities. In this research, we introduce a novel model called BKMR, designed to analyze and predict communicable disease occurrences in Machakos County. This study underscores the significance of data-driven approaches and outlines a plan to evaluate prediction accuracy through empirical analysis, with a particular focus on comparing BKMR with existing models using the R statistical software. We highlight the differences between estimated parameters and actual observations, emphasizing aspects not present in the training dataset. Our findings demonstrate that BKMR outperforms the Poisson regression model, offering greater flexibility and robustness. Moreover, it provides the ability to quantify uncertainty in model parameters, enhancing the capacity to make inferences about the real world. This research has substantial implications for healthcare management and disease control efforts in Machakos County.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya
AU  - Cecilia Mbithe Titus
AU  - Anthony Wanjoya
AU  - Thomas Mageto
Y1  - 2023/10/31
PY  - 2023
N1  - https://doi.org/10.11648/j.ijdsa.20230902.13
DO  - 10.11648/j.ijdsa.20230902.13
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  - 43
EP  - 49
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20230902.13
AB  - Effective management and control of communicable diseases are paramount for healthcare managers. Data-driven analysis plays a crucial role in understanding and curbing the spread of such diseases. While numerous epidemiological models have been developed to explain disease spread, many lack the incorporation of prior and posterior probabilities. In this research, we introduce a novel model called BKMR, designed to analyze and predict communicable disease occurrences in Machakos County. This study underscores the significance of data-driven approaches and outlines a plan to evaluate prediction accuracy through empirical analysis, with a particular focus on comparing BKMR with existing models using the R statistical software. We highlight the differences between estimated parameters and actual observations, emphasizing aspects not present in the training dataset. Our findings demonstrate that BKMR outperforms the Poisson regression model, offering greater flexibility and robustness. Moreover, it provides the ability to quantify uncertainty in model parameters, enhancing the capacity to make inferences about the real world. This research has substantial implications for healthcare management and disease control efforts in Machakos County.

VL  - 9
IS  - 2
ER  -

Copy | Download