A data-driven approach to hepatitis C forecasting using machine learning and epidemiological models
- 1Abdus Salam School of Mathematical Sciences, Government College University, Lahore, Pakistan
- 2Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- 3Department of Public Health Faculty of Health Science Salaam University, Mogadishu, Somalia
Abstract
The hepatitis C virus is a significant global health concern and a major cause of chronic liver disease. Therefore, developing a mathematical model is essential for understanding, controlling and managing its transmission dynamics. In this paper, we developed a mathematical model and simulated the dynamics of transmission of the hepatitis C virus, considering alcohol use, a key factor contributing to increased damage to the liver of infected individuals. Computational investigation of nonlinear biological models by traditional numerical solvers is challenging due to their nonlinearity and inherent complexity. Furthermore, we proposed a deep learning-based technique to solve the nonlinear differential equations governing the model for transmission of the hepatitis C virus in six compartments. The proposed deep learning-based model is compared with other established methods like the Runge-Kutta method and Livermore solver for ordinary differential equations algorithm to validate its efficacy and accuracy in predicting hepatitis C dynamics. The basic reproduction number, R0, is calculated and stability is analyzed at both the disease-free and endemic equilibrium points. Sensitivity analysis is performed to determine key parameters that contribute to hepatitis C transmission. The results indicate that the proposed approach is an efficient and robust solution with better convergence and stability than existing methods. This study highlights the potential of deep learning methods in epidemiology as a promising tool for predicting and controlling infectious diseases such as hepatitis C, particularly in the presence of behavioral risk factors such as alcohol consumption.
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BibTeX
@article{mannan2026datadriven,
title = {{A data-driven approach to hepatitis C forecasting using machine learning and epidemiological models}},
author = {Abdul Mannan and Jamshaid Ul Rahman and Ebraheem Alzahrani and Osman Abubakar Fiidow},
journal = {PLOS One},
year = {2026},
month = aug,
doi = {10.1371/journal.pone.0357173},
url = {https://doi.org/10.1371/journal.pone.0357173},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A data-driven approach to hepatitis C forecasting using machine learning and epidemiological models
AU - Abdul Mannan
AU - Jamshaid Ul Rahman
AU - Ebraheem Alzahrani
AU - Osman Abubakar Fiidow
PY - 2026
DA - 2026/08/31
JO - PLOS One
DO - 10.1371/journal.pone.0357173
UR - https://doi.org/10.1371/journal.pone.0357173
AB - The hepatitis C virus is a significant global health concern and a major cause of chronic liver disease. Therefore, developing a mathematical model is essential for understanding, controlling and managing its transmission dynamics. In this paper, we developed a mathematical model and simulated the dynamics of transmission of the hepatitis C virus, considering alcohol use, a key factor contributing to increased damage to the liver of infected individuals. Computational investigation of nonlinear biological models by traditional numerical solvers is challenging due to their nonlinearity and inherent complexity. Furthermore, we proposed a deep learning-based technique to solve the nonlinear differential equations governing the model for transmission of the hepatitis C virus in six compartments. The proposed deep learning-based model is compared with other established methods like the Runge-Kutta method and Livermore solver for ordinary differential equations algorithm to validate its efficacy and accuracy in predicting hepatitis C dynamics. The basic reproduction number, R0, is calculated and stability is analyzed at both the disease-free and endemic equilibrium points. Sensitivity analysis is performed to determine key parameters that contribute to hepatitis C transmission. The results indicate that the proposed approach is an efficient and robust solution with better convergence and stability than existing methods. This study highlights the potential of deep learning methods in epidemiology as a promising tool for predicting and controlling infectious diseases such as hepatitis C, particularly in the presence of behavioral risk factors such as alcohol consumption.
SN - 1932-6203
ER -