Research Article | | Peer-Reviewed

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

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), 2023. Published by Science Publishing Group

Keywords

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

References
[1] Ali, H., Nwaosu, S. C., Lasisi, K. E & Abdulkadir, A. (2022). Bayesian Information Based Criteria for Poisson Regression Models. International Journal of Formal Sciences, 13 (1); 22-40.
[2] Atonio, C. & Dunson, D. B. (2011). Bayesian Kernel Mixtures for Counts. Journal of AM Statistical Association, 106 (496); 1528-1539. doi: 10.1198/jasa.2011.tm10552.
[3] Arti, S. (2021). Bayesian Kernel Methods: Applications in Medical Diagnosis Decision-Making Process; A Case Study. International Journal of Big Data and Analytics in Healthcare, 6 (1); 26-39. doi: 10.4018/IJBDAH.20210101-oa3.
[4] Areti, B., Bennett, J. E. & Marta, B. (2018). A Bayesian Mixture Modeling Approach for Public Health Surveillance. Biostatistics, 00 (00); 1-15. doi: 10.1093/biostatistics/kxy038.
[5] Baroud, H. & Barkar, K. (2018). A Bayesian Kernel Approach to Modelling Resilience-Based Network Component Importance. Reliability Engineering and System Safety, 170 (1); 10-19. doi: 10.1016/j.ress.2017.09.022.
[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.
[11] Gyeyoon, Y., Yuting, W., Caitlin, G. H. & Megan, E. R. (2022). Exposure to Metal Mixtures in Association with Cardiovascular Risk Factors and Outcomes: A Scoping Review. Toxics, 10 (116); 1-32. doi: 10.3390/toxics10030116.
[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.
Cite This Article
  • 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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    T1  - Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya
    AU  - Cecilia Mbithe Titus
    AU  - Anthony Wanjoya
    AU  - Thomas Mageto
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    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
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Author Information
  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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

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

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