Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression

W. S. Winahju; A. Mukarromah; S. Putri

doi:10.1063/1.4914446

Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression

W. S. Winahju^*
, A. Mukarromah
, S. Putri

^*Corresponding author for this work

Laboratory of Computational Statistics and Data Science

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Leprosy is a chronic infectious disease caused by bacteria of leprosy (Mycobacterium leprae). Leprosy has become an important thing in Indonesia because its morbidity is quite high. Based on WHO data in 2014, in 2012 Indonesia has the highest number of new leprosy patients after India and Brazil with a contribution of 18.994 people (8.7% of the world). This number makes Indonesia automatically placed as the country with the highest number of leprosy morbidity of ASEAN countries. The province that most contributes to the number of leprosy patients in Indonesia is East Java. There are two kind of leprosy. They consist of pausibacillary and multibacillary. The morbidity of multibacillary leprosy is higher than pausibacillary leprosy. This paper will discuss modeling both of the number of multibacillary and pausibacillary leprosy patients as responses variables. These responses are count variables, so modeling will be conducted by using bivariate poisson regression method. Unit experiment used is in East Java, and predictors involved are: environment, demography, and poverty. The model uses data in 2012, and the result indicates that all predictors influence significantly.

Original language	English
Title of host publication	Symposium on Biomathematics, SYMOMATH 2014
Editors	Thomas Gotz, Agus Suryanto
Publisher	American Institute of Physics Inc.
Pages	147-152
Number of pages	6
ISBN (Electronic)	9780735412934
DOIs	https://doi.org/10.1063/1.4914446
Publication status	Published - 2015
Event	2nd International Symposium on Biomathematics, SYMOMATH 2014 - Malang, East Java, Indonesia Duration: 31 Aug 2014 → 2 Sept 2014

Publication series

Name	AIP Conference Proceedings
Volume	1651
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	2nd International Symposium on Biomathematics, SYMOMATH 2014
Country/Territory	Indonesia
City	Malang, East Java
Period	31/08/14 → 2/09/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

bivariate poisson regression
leprosy
multibacillary
paucibacillary

Access to Document

10.1063/1.4914446

Cite this

Winahju, W. S., Mukarromah, A., & Putri, S. (2015). Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression. In T. Gotz, & A. Suryanto (Eds.), Symposium on Biomathematics, SYMOMATH 2014 (pp. 147-152). (AIP Conference Proceedings; Vol. 1651). American Institute of Physics Inc.. https://doi.org/10.1063/1.4914446

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title = "Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression",

abstract = "Leprosy is a chronic infectious disease caused by bacteria of leprosy (Mycobacterium leprae). Leprosy has become an important thing in Indonesia because its morbidity is quite high. Based on WHO data in 2014, in 2012 Indonesia has the highest number of new leprosy patients after India and Brazil with a contribution of 18.994 people (8.7\% of the world). This number makes Indonesia automatically placed as the country with the highest number of leprosy morbidity of ASEAN countries. The province that most contributes to the number of leprosy patients in Indonesia is East Java. There are two kind of leprosy. They consist of pausibacillary and multibacillary. The morbidity of multibacillary leprosy is higher than pausibacillary leprosy. This paper will discuss modeling both of the number of multibacillary and pausibacillary leprosy patients as responses variables. These responses are count variables, so modeling will be conducted by using bivariate poisson regression method. Unit experiment used is in East Java, and predictors involved are: environment, demography, and poverty. The model uses data in 2012, and the result indicates that all predictors influence significantly.",

keywords = "bivariate poisson regression, leprosy, multibacillary, paucibacillary",

author = "Winahju, \{W. S.\} and A. Mukarromah and S. Putri",

note = "Publisher Copyright: {\textcopyright} 2015 AIP Publishing LLC.; 2nd International Symposium on Biomathematics, SYMOMATH 2014 ; Conference date: 31-08-2014 Through 02-09-2014",

year = "2015",

doi = "10.1063/1.4914446",

language = "English",

series = "AIP Conference Proceedings",

publisher = "American Institute of Physics Inc.",

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}

Winahju, WS, Mukarromah, A & Putri, S 2015, Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression. in T Gotz & A Suryanto (eds), Symposium on Biomathematics, SYMOMATH 2014. AIP Conference Proceedings, vol. 1651, American Institute of Physics Inc., pp. 147-152, 2nd International Symposium on Biomathematics, SYMOMATH 2014, Malang, East Java, Indonesia, 31/08/14. https://doi.org/10.1063/1.4914446

Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression. / Winahju, W. S.; Mukarromah, A.; Putri, S.
Symposium on Biomathematics, SYMOMATH 2014. ed. / Thomas Gotz; Agus Suryanto. American Institute of Physics Inc., 2015. p. 147-152 (AIP Conference Proceedings; Vol. 1651).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression

AU - Winahju, W. S.

AU - Mukarromah, A.

AU - Putri, S.

PY - 2015

Y1 - 2015

N2 - Leprosy is a chronic infectious disease caused by bacteria of leprosy (Mycobacterium leprae). Leprosy has become an important thing in Indonesia because its morbidity is quite high. Based on WHO data in 2014, in 2012 Indonesia has the highest number of new leprosy patients after India and Brazil with a contribution of 18.994 people (8.7% of the world). This number makes Indonesia automatically placed as the country with the highest number of leprosy morbidity of ASEAN countries. The province that most contributes to the number of leprosy patients in Indonesia is East Java. There are two kind of leprosy. They consist of pausibacillary and multibacillary. The morbidity of multibacillary leprosy is higher than pausibacillary leprosy. This paper will discuss modeling both of the number of multibacillary and pausibacillary leprosy patients as responses variables. These responses are count variables, so modeling will be conducted by using bivariate poisson regression method. Unit experiment used is in East Java, and predictors involved are: environment, demography, and poverty. The model uses data in 2012, and the result indicates that all predictors influence significantly.

AB - Leprosy is a chronic infectious disease caused by bacteria of leprosy (Mycobacterium leprae). Leprosy has become an important thing in Indonesia because its morbidity is quite high. Based on WHO data in 2014, in 2012 Indonesia has the highest number of new leprosy patients after India and Brazil with a contribution of 18.994 people (8.7% of the world). This number makes Indonesia automatically placed as the country with the highest number of leprosy morbidity of ASEAN countries. The province that most contributes to the number of leprosy patients in Indonesia is East Java. There are two kind of leprosy. They consist of pausibacillary and multibacillary. The morbidity of multibacillary leprosy is higher than pausibacillary leprosy. This paper will discuss modeling both of the number of multibacillary and pausibacillary leprosy patients as responses variables. These responses are count variables, so modeling will be conducted by using bivariate poisson regression method. Unit experiment used is in East Java, and predictors involved are: environment, demography, and poverty. The model uses data in 2012, and the result indicates that all predictors influence significantly.

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KW - multibacillary

KW - paucibacillary

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DO - 10.1063/1.4914446

M3 - Conference contribution

AN - SCOPUS:84994410007

T3 - AIP Conference Proceedings

SP - 147

EP - 152

BT - Symposium on Biomathematics, SYMOMATH 2014

A2 - Gotz, Thomas

A2 - Suryanto, Agus

PB - American Institute of Physics Inc.

T2 - 2nd International Symposium on Biomathematics, SYMOMATH 2014

Y2 - 31 August 2014 through 2 September 2014

ER -

Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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