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Geographically Weighted Poisson Regression Semiparametric On Modeling Of The Number Of Tuberculosis Cases (Case Study: Bandung City)

Geographically Weighted Poisson Regression Semiparametric On Modeling Of The Number Of Tuberculosis Cases (Case Study: Bandung City)
Octavianty, Toni Toharudin, I G N Mindra Jaya
Universitas Padjadjaran, Published by the American Institute of Physics, Citation: AIP Conference Proceedings 1827, 020022 (2017); doi: 10.1063/1.4979438 View online: http://dx.doi.org/10.1063/1.4979438, View Table of Contents: http://aip.scitation.org/toc/apc/1827/1
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Universitas Padjadjaran, Published by the American Institute of Physics, Citation: AIP Conference Proceedings 1827, 020022 (2017); doi: 10.1063/1.4979438 View online: http://dx.doi.org/10.1063/1.4979438, View Table of Contents: http://aip.scitation.org/toc/apc/1827/1

Tuberculosis (TB) is a disease caused by a bacterium, called Mycobacterium tuberculosis, which typically attacks the lungs but can also affect the kidney, spine, and brain (Centers for Disease Control and Prevention). Indonesia had the lar gest number of TB cas es after India ( Global Tuberculosis Report 2015 by WHO). The distribution of Mycobacterium tuberculosis genotypes in Indonesia showed the high genetic diversit y and tended to vary by geographic regions. For instance, in Bandung city, the prevalence rate of TB morbidity is quite high. A number of TB patients belong to the counted data. To determine the factors that significantly influence the number of tuberculosis patients in each location of the observations can be used statistical analysis tool that is Geographically Weighted Poisson Regression Semiparametric (GWPRS). GWPRS is an extension of the Poisson regression and GWPR that is influenced by geographical factors, and there is also variables that influence globally and locally. Using the TB Data in Bandung city (in 2015), the results show that the global and local variables that influence the number of tuberculosis patients in every sub-district.

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