American Journal of Theoretical and Applied Statistics

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Poisson Inverse Gaussian (PIG) Model for Infectious Disease Count Data

Received: 09 September 2016    Accepted: 21 September 2016    Published: 10 October 2016
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Abstract

Traditionally, statistical models provide a general basis for analysis of infectious disease count data with its unique characteristics such as low disease counts, underreporting, reporting delays, seasonality, past outbreaks and lack of a number of susceptible. Through this approach, statistical models have provided a popular means of estimating safety performance of various health elements. Predictions relating to infectious disease outbreaks by use of statistical models have been based on Poisson modeling framework and Negative Binomial (NB) modeling framework in the case of overdispersion within the count data. Recent studies have proved that the Poisson- Inverse Gaussian (PIG) model can be used to analyze count data that is highly overdispersed which cannot be effectively analyzed by the traditional Negative Binomial model. A PIG model with fixed/varying dispersion parameters is fitted to two infectious disease datasets and its performance in terms of goodness-of-fit and future outbreak predictions of infectious disease is compared to that of the traditional NB model.

DOI 10.11648/j.ajtas.20160505.22
Published in American Journal of Theoretical and Applied Statistics (Volume 5, Issue 5, September 2016)
Page(s) 326-333
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

Mixed Models, Poisson-Inverse Gaussian Distribution, Negative Binomial Distribution, Infectious Disease

References
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Author Information
  • Applied Statistics, Department of Statistics and Actuarial Sciences, College of Pure and Applied Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, College of Pure and Applied Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, College of Pure and Applied Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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    Vincent Moshi Ouma, Samuel Musili Mwalili, Anthony Wanjoya Kiberia. (2016). Poisson Inverse Gaussian (PIG) Model for Infectious Disease Count Data. American Journal of Theoretical and Applied Statistics, 5(5), 326-333. https://doi.org/10.11648/j.ajtas.20160505.22

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    ACS Style

    Vincent Moshi Ouma; Samuel Musili Mwalili; Anthony Wanjoya Kiberia. Poisson Inverse Gaussian (PIG) Model for Infectious Disease Count Data. Am. J. Theor. Appl. Stat. 2016, 5(5), 326-333. doi: 10.11648/j.ajtas.20160505.22

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    AMA Style

    Vincent Moshi Ouma, Samuel Musili Mwalili, Anthony Wanjoya Kiberia. Poisson Inverse Gaussian (PIG) Model for Infectious Disease Count Data. Am J Theor Appl Stat. 2016;5(5):326-333. doi: 10.11648/j.ajtas.20160505.22

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  • @article{10.11648/j.ajtas.20160505.22,
      author = {Vincent Moshi Ouma and Samuel Musili Mwalili and Anthony Wanjoya Kiberia},
      title = {Poisson Inverse Gaussian (PIG) Model for Infectious Disease Count Data},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {5},
      number = {5},
      pages = {326-333},
      doi = {10.11648/j.ajtas.20160505.22},
      url = {https://doi.org/10.11648/j.ajtas.20160505.22},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20160505.22},
      abstract = {Traditionally, statistical models provide a general basis for analysis of infectious disease count data with its unique characteristics such as low disease counts, underreporting, reporting delays, seasonality, past outbreaks and lack of a number of susceptible. Through this approach, statistical models have provided a popular means of estimating safety performance of various health elements. Predictions relating to infectious disease outbreaks by use of statistical models have been based on Poisson modeling framework and Negative Binomial (NB) modeling framework in the case of overdispersion within the count data. Recent studies have proved that the Poisson- Inverse Gaussian (PIG) model can be used to analyze count data that is highly overdispersed which cannot be effectively analyzed by the traditional Negative Binomial model. A PIG model with fixed/varying dispersion parameters is fitted to two infectious disease datasets and its performance in terms of goodness-of-fit and future outbreak predictions of infectious disease is compared to that of the traditional NB model.},
     year = {2016}
    }
    

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    T1  - Poisson Inverse Gaussian (PIG) Model for Infectious Disease Count Data
    AU  - Vincent Moshi Ouma
    AU  - Samuel Musili Mwalili
    AU  - Anthony Wanjoya Kiberia
    Y1  - 2016/10/10
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    AB  - Traditionally, statistical models provide a general basis for analysis of infectious disease count data with its unique characteristics such as low disease counts, underreporting, reporting delays, seasonality, past outbreaks and lack of a number of susceptible. Through this approach, statistical models have provided a popular means of estimating safety performance of various health elements. Predictions relating to infectious disease outbreaks by use of statistical models have been based on Poisson modeling framework and Negative Binomial (NB) modeling framework in the case of overdispersion within the count data. Recent studies have proved that the Poisson- Inverse Gaussian (PIG) model can be used to analyze count data that is highly overdispersed which cannot be effectively analyzed by the traditional Negative Binomial model. A PIG model with fixed/varying dispersion parameters is fitted to two infectious disease datasets and its performance in terms of goodness-of-fit and future outbreak predictions of infectious disease is compared to that of the traditional NB model.
    VL  - 5
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    ER  - 

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