Modelling the Number of Tuberculosis (TB) Cases in Indonesia using Poisson Regression and Negative Binomial Regression
- 10.2991/assehr.k.201010.007How to use a DOI?
- Negative Binomial Regression, Poisson Regression, Tuberculosis
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.
- © 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/).
Cite this article
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 -