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Predicting Number of People Living with Chronic HCV Using Gray-Weighted Markov Chain Model \GW-MCM\
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Predicting Number of People Living with Chronic HCV Using Gray-Weighted Markov Chain Model \GW-MCM\
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Journal Article
Predicting Number of People Living with Chronic HCV Using Gray-Weighted Markov Chain Model \GW-MCM\
2023
Overview
Hepatitis C virus (HCV) infection is an important global health problem, with millions of people affected by chronic HCV worldwide. Accurate prediction of the number of individuals living with chronic HCV in Iraq is essential for public health planning, resource allocation, and the development of effective intervention strategies. This paper proposes a different approach to predicting chronic HCV prevalence using a weighted-gray Markov chain model. The GW-MCM model integrates Markov chain modelling principles with gray system theory, providing a powerful framework for capturing the dynamics of chronic HCV spread. The model takes into account multiple factors, including HCV transmission rate, treatment rates, and demographic changes. Data from multiple sources, including epidemiological studies, health care records, and population distribution, are integrated into the model to ensure accuracy. This research uses historical data to calibrate and validate the gray-weighted Markov chain model, ensuring its reliability in predicting the spread of chronic HCV over time. Data for this study were obtained from the website: https://www.globalhep.org/countryprogress/iraq. The data shows the number of people infected with the disease in Iraq for the years 1990-2019. The results show that the prediction accuracy of the GW-MCM model is much higher than the GM model (1,1), and the number of people living with chronic HCV for the years 2020, 2021, and 2022 is not constant, meaning that it increases and decreases until 2025, then begins to increase. After 2025, it begins to decline until it reaches a stable level.
Publisher
جامعة كركوك - كلية الإدارة والاقتصاد
Subject
