Altitude Factors Affect Dengue Fever Cases in South Sulawesi: A Study Using Poisson Inverse Gaussian Regression Model

  • Adiatma Adiatma Bagian Matematika, Universitas Islam Negeri Alauddin, Makassar
    (ID)
  • Amin Tohari Bagian Matematika, Universitas Nusantara PGRI, Kediri
    (ID)
  • Ali Faisal Bagian Matematika, Universitas Islam Negeri Alauddin, Makassar
    (ID)
  • Syamsul Alam Bagian Kesehatan Masyarakat, Universitas Islam Negeri Alauddin, Makassar
    (ID)
Keywords: altitude factor, dfh case, dengue fever, poisson Inverse gaussian regression

Abstract

Poisson regression is used to model enumeration data such as data on the number of DHF cases. This model has the assumption that is fulfilled is the average and the variance must have the same value or it is called the equidispersion. But this assumption is not fulfilled because the data on the number of dengue cases experienced violations of this assumption. The violation is that the average value is smaller than the variance value or it is called overdispersion. This results in incorrect conclusions because the prediction standard error is underestimated. The way to prevent this is by combining the Poisson distribution and discrete or continuous distribution, this combination is called Mixed Poisson Distribution. Researchers use one of the Mixed Poisson methods, namely Inverse Gaussian Poisson Regression (PIG) because the method is used when the data is overdispersed and the parameters are known or close form on the likelihood function. Based on the results of the study, it is known that the height of the area   is a factor that significantly influences DHF cases in South Sulawesi.

 

 

   

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Author Biography

Syamsul Alam, Bagian Kesehatan Masyarakat, Universitas Islam Negeri Alauddin, Makassar
Universitas Islam Negeri Alauddin Makassar

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Published
2021-02-28
How to Cite
Adiatma, A., Tohari, A., Faisal, A., & Alam, S. (2021). Altitude Factors Affect Dengue Fever Cases in South Sulawesi: A Study Using Poisson Inverse Gaussian Regression Model. Diversity: Disease Preventive of Research Integrity, 1(2), 58-63. https://doi.org/10.24252/diversity.v1i2.19764
Section
Volume 1, Issue 2, 2021
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