ANALISIS JUMLAH KASUS MALARIA DI WILAYAH SUMATERA MENGGUNAKAN GEOGRAPHICALLY WEIGHTED ZERO-INFLATED POISSON REGRESSION (GWZIPR)

Authors

  • Rahmat Kevin Praditia Program Studi S1 Statistika Universitas Bengkulu (UNIB), Indonesia
  • Dian Agustina Program Studi S1 Statistika Universitas Bengkulu (UNIB), Indonesia
  • Dyah Setyo Rini Program Studi S1 Statistika Universitas Bengkulu (UNIB), Indonesia

DOI:

https://doi.org/10.29244/ijsa.v4i4.716

Keywords:

AIC, GWZIPR, spatial, overdispersion, ZIP Regression

Abstract

A method that can be used if there is a spatial factor and if overdispersion happens in a count data is Geographically Weighted Zero-Inflated Poisson Regression (GWZIPR). This research aimed to analyze the number of malaria cases in every regency/city of Sumatra Land using the GWZIPR method and distribution mapping of factors affecting the number of malaria cases in Sumatra Land. Data involved in this research was the number of malaria cases as the response variable and the predictor variable as a percentage of households that have access to proper sanitation, a percentage of households that have access to proper water resources, and a percentage of the number of public health centers. The results were for each area which had distinctive models based on significant variables. The distribution mapping of factors affecting the number of malaria cases in every regency/city was commonly divided into three groups based on significant variables on ln and logit models. The mapping did not shape a spreading pattern or each regency/city in that group because the geographical locations were close to each other. GWZIPR method in this research was better than the ZIP Regression method because it produced the least AIC value.

Downloads

Download data is not yet available.

References

Agresti, A. (1990). Categorical data analysis. New York (US): John Wiley & Sons.

Cameron, A. C., & Trivedi, P. K. (1990). Regression Analysis of Count Data. New York (US): Cambridge University Press.

[DIRJEN PPPL] Direktur Jenderal Pengendalian Penyakit dan Penyehatan Lingkungan. (2014). Pedoman Manajemen Malaria. Jakarta (ID): Mitra Besari.

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2014). Geographically Weighted Regression The Analysis of Spatially Varying Relationships. The Atrium Southern Gate England (UK): John Wiley & Sons.

Gujarati, D. N. (2004). Basic Econometrics (4th ed). New York (US): The McGraw-Hill Companies.

Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed). New York (US): Cambridge University Press.

[KEMENKES] Kementerian Kesehatan RI. (2011). Epidemiologi Malaria di Indonesia. Jakarta (ID): Jendela Data dan Informasi Kesehatan.

Kutner, M. H., Nachsheim, C. J., Neter, J., & Li, W. (2005). Applied Linear Statistical Models (5th ed). New York (US): Mc Graw Hill.

Lambert, D. (1992). Zero-Inflated Poisson Regression, With an Application to Defects in Manufacturing. Technometrics, 32(1): 1–14.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed). New Jersey (US): John Wiley & Sons.

Purhadi, Dewi, Y. S., & Amaliana, L. (2015). Zero Inflated Poisson and Geographically Weighted Zero-Inflated Poisson Regression Model: Application to Elephantiasis (Filariasis) Counts Data. Journal of Mathematics and Statistics, 11(2): 52–60.

Siswanto, S., & Thamrin, S. A. (2020). Penentuan faktor-faktor potensial yang mempengaruhi kejadian malaria di Provinsi Papua dengan epidemiologi spasial. Indonesian Journal of Statistics and Its Applications, 4(3): 498–509.

Downloads

Published

2020-12-25

How to Cite

Praditia, R. K., Agustina, D., & Rini, D. S. (2020). ANALISIS JUMLAH KASUS MALARIA DI WILAYAH SUMATERA MENGGUNAKAN GEOGRAPHICALLY WEIGHTED ZERO-INFLATED POISSON REGRESSION (GWZIPR). Indonesian Journal of Statistics and Its Applications, 4(4), 638–648. https://doi.org/10.29244/ijsa.v4i4.716

Issue

Section

Articles