Relative risk estimation of tuberculosis disease mapping with stochastic equations

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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Main Author: Ijlal, Mohd Diah
Format: Thesis
Language:eng
eng
Published: 2017
Subjects:
QA273-280 Probabilities
Mathematical statistics
Online Access:https://etd.uum.edu.my/6989/1/s818270_01.pdf
https://etd.uum.edu.my/6989/2/s818270_02.pdf
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id my-uum-etd.6989
record_format uketd_dc
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Aziz, Nazrina
Mat Kasim, Maznah
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
qualification_name other
qualification_level Master's degree
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
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2017
url https://etd.uum.edu.my/6989/1/s818270_01.pdf
https://etd.uum.edu.my/6989/2/s818270_02.pdf
_version_ 1747828141445873664
spelling my-uum-etd.69892021-05-09T03:47:20Z Relative risk estimation of tuberculosis disease mapping with stochastic equations 2017 Ijlal, Mohd Diah Aziz, Nazrina Mat Kasim, Maznah Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences 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 https://etd.uum.edu.my/6989/ https://etd.uum.edu.my/6989/1/s818270_01.pdf text eng public https://etd.uum.edu.my/6989/2/s818270_02.pdf text eng public other masters Universiti Utara Malaysia Abdul Karim Iddrisu and Amoako, Y. A. (2016). Spatial Modeling and Mapping of Tuberculosis Using Bayesian Hierarchical Approaches. Open Journal of Statistics, 6(June), 482–513. doi:10.4236/ojs.2016.63043 Abramson, G. (2001). Mathematical modeling of the spread of infectious diseases. A series of lectures given at PANDA, University of New Mexico. Retrieved from https://pdfs.semanticscholar.org/938d/ a251d4b0152d7f27175d54a0e595dc5258d9.pdf Achterberg, J. T. (2009). 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