Modelling the Number of Tuberculosis (TB) Cases in Indonesia using Poisson Regression and Negative Binomial Regression

DOI

10.2991/assehr.k.201010.007How to use a DOI?

Keywords

Negative Binomial Regression, Poisson Regression, Tuberculosis

Abstract

Tuberculosis (TB) is still one of the world’s health problems which continues to be overcome today. Indonesia is a country that accounts for 8% of the number of TB cases in the world in the third highest position after India and China. Tuberculosis is one of the lower respiratory and infectious diseases caused by the bacterium Mycrobacterium Tuberculosis. In this research, modelling the number of Tuberculosis cases was carried out to suppress the increase in the number of TB cases in the Indonesia with the Negative Binomial regression approach. Data on the number of TB cases is the count data so the analysis used to model the count data is Poisson regression. However, in analysis there is often an overdispersion phenomenon which will cause the estimation results to be biased. So that needs to be overcome by Negative Binomial regression, the results obtained are factors that have an influence on the number of TB cases in Indonesia are the percentage of districts/cities in Indonesia with a healthy environmental quality, the number of HIV (Positive) cases, the number of AIDS cases, and the percentage of households which has access to adequate drinking water sources with Pseudo-R 2 of 64.13% and the AIC of the Negative Binomial regression model is 724.33.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Rahmadi Yotenka
AU  - Alfazrin Banapon
PY  - 2020
DA  - 2020/10/11
TI  - Modelling the Number of Tuberculosis (TB) Cases in Indonesia using Poisson Regression and Negative Binomial Regression
BT  - Proceedings of the 2nd International Seminar on Science and Technology (ISSTEC 2019)
PB  - Atlantis Press
SP  - 36
EP  - 42
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.201010.007
DO  - 10.2991/assehr.k.201010.007
ID  - Yotenka2020
ER  -