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Relative risk estimation of tuberculosis disease mapping with stochastic equations

Tuberculosis (TB) is one of the death leading causes in developing countries. The current monitoring of the disease is based only on the total cases reported. Alternatively, a better approach called disease mapping offers geographic distribution of the disease relative risk. Previous studies used St...

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主要作者: Ijlal, Mohd Diah
格式: Thesis
語言:English
English
出版: 2017
主題:
QA273-280 Probabilities. Mathematical statistics
在線閱讀:http://etd.uum.edu.my/6989/
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spelling my.uum.etd.69892021-05-09T03:47:20Z http://etd.uum.edu.my/6989/ Relative risk estimation of tuberculosis disease mapping with stochastic equations Ijlal, Mohd Diah QA273-280 Probabilities. Mathematical statistics Tuberculosis (TB) is one of the death leading causes in developing countries. The current monitoring of the disease is based only on the total cases reported. Alternatively, a better approach called disease mapping offers geographic distribution of the disease relative risk. Previous studies used Standard Mortality Ratio (SMR) and Poisson-gamma models to estimate relative risk but these models have several drawbacks. SMR model cannot detect relative risk for small areas while Poisson-gamma model cannot allow for covariate adjustments. Hence, the objective of this study is to develop an alternative statistical model in estimating the relative risk called stochastic Susceptible-Latently infected-Infectious-Recovered (SLIR). There are four phases in this study. Firstly, the deterministic SLIR model for TB disease transmission is developed. Then, the stochastic SLIR model is constructed. Next, the stochastic SLIR model is used to estimate the relative risk for the disease. Later, the performance of the stochastic SLIR model is compared with other existing models based on relative risk values. Finally, the TB risk maps are constructed. For numerical analysis, this study used a data set of Malaysia TB cases reported from 2008 to 2015. Findings show that there is a large difference of relative risk estimation values when using stochastic SLIR model compared to existing models. This is clearly visible through disease mapping as some locations change colour from low tone (low risk) to darker tone (higher risk). This is due to the inclusion of latent component in the stochastic SLIR model. As a conclusion, this study offers a better model in estimating relative risk for TB disease. The findings may assist the government in prioritizing locations which need further attention especially in terms of health policy and financial support. 2017 Thesis NonPeerReviewed text en /6989/1/s818270_01.pdf text en /6989/2/s818270_02.pdf Ijlal, Mohd Diah (2017) Relative risk estimation of tuberculosis disease mapping with stochastic equations. Masters thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
topic QA273-280 Probabilities. Mathematical statistics
spellingShingle QA273-280 Probabilities. Mathematical statistics
Ijlal, Mohd Diah
Relative risk estimation of tuberculosis disease mapping with stochastic equations
description Tuberculosis (TB) is one of the death leading causes in developing countries. The current monitoring of the disease is based only on the total cases reported. Alternatively, a better approach called disease mapping offers geographic distribution of the disease relative risk. Previous studies used Standard Mortality Ratio (SMR) and Poisson-gamma models to estimate relative risk but these models have several drawbacks. SMR model cannot detect relative risk for small areas while Poisson-gamma model cannot allow for covariate adjustments. Hence, the objective of this study is to develop an alternative statistical model in estimating the relative risk called stochastic Susceptible-Latently infected-Infectious-Recovered (SLIR). There are four phases in this study. Firstly, the deterministic SLIR model for TB disease transmission is developed. Then, the stochastic SLIR model is constructed. Next, the stochastic SLIR model is used to estimate the relative risk for the disease. Later, the performance of the stochastic SLIR model is compared with other existing models based on relative risk values. Finally, the TB risk maps are constructed. For numerical analysis, this study used a data set of Malaysia TB cases reported from 2008 to 2015. Findings show that there is a large difference of relative risk estimation values when using stochastic SLIR model compared to existing models. This is clearly visible through disease mapping as some locations change colour from low tone (low risk) to darker tone (higher risk). This is due to the inclusion of latent component in the stochastic SLIR model. As a conclusion, this study offers a better model in estimating relative risk for TB disease. The findings may assist the government in prioritizing locations which need further attention especially in terms of health policy and financial support.
format Thesis
author Ijlal, Mohd Diah
author_facet Ijlal, Mohd Diah
author_sort Ijlal, Mohd Diah
title Relative risk estimation of tuberculosis disease mapping with stochastic equations
title_short Relative risk estimation of tuberculosis disease mapping with stochastic equations
title_full Relative risk estimation of tuberculosis disease mapping with stochastic equations
title_fullStr Relative risk estimation of tuberculosis disease mapping with stochastic equations
title_full_unstemmed Relative risk estimation of tuberculosis disease mapping with stochastic equations
title_sort relative risk estimation of tuberculosis disease mapping with stochastic equations
publishDate 2017
url http://etd.uum.edu.my/6989/
_version_ 1701165216498188288
score 13.251813

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