JOURNAL ARTICLE

PEMODELAN JUMLAH KASUS DEMAM BERDARAH DENGUE (DBD) DI JAWA TENGAH DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR)

Abstract

Dengue Hemorrhagic Fever (DHF) is one of the diseases with unsual occurrence in Central Java and spread throughout the regency/city. The number sufferers of this disease is still high because the mortality rate is still above the national target. Regarding the less handling of DHF spread, it is necessary to make a plan by identify the factors that allegedly affect that case. Characteristics of data the DHF cases is count data, so this research is carried out using poisson regression. If in poisson regression there is overdispersion, it can be overcome using negative binomial regression. Meanwhile to see the spatial effect, we can use the Geographically Weighted Negative Binomial Regression (GWNBR) method. GWNBR modeling uses a fixed exponential kernel for weighting function. GWNBR is better at modeling the number of DHF cases because it has the smallest AIC value than poisson regression and negative binomial regression. The results of research with poisson regression obtained three variables that have a significant effect on dengue cases. For negative binomial regression, two variables have a significant effect on DHF cases. While the GWNBR method obtained two groups of districts/cities based on significant variables. The variables affecting the number of DHF cases in all districts/cities in Central Java are the percentage of healthy houses, the percentage of clean water quality, and the ratio of medical personnel.Keywords: DHF, GWNBR, Poisson Regression, Binomial Negative Regression, Fixed Exponential Kernel

Keywords:
Negative binomial distribution Poisson regression Statistics Regression analysis Mathematics Dengue hemorrhagic fever Overdispersion Count data Binomial regression Regression Poisson distribution Dengue fever Medicine Population Environmental health

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Topics

Dengue and Mosquito Control Research
Social Sciences →  Social Sciences →  Sociology and Political Science
Data Mining and Machine Learning Applications
Physical Sciences →  Computer Science →  Information Systems
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