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Dynamics-Informed Neural Network Modeling of COVID19 Transmission in Afghanistan Using the SEIRV Framework.docx
AJID-O07 Movahedi for edit 2 july 2025.docx
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9- AJID-O07_Movahedi RTG 2_july_2025 (1).pdf
9- AJID-O07_Movahedi RTG 2_july_2025 (1).pdf
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10.60141/ajid.114
Dynamics-Informed Neural Network Modeling of COVID-19 Transmission in Afghanistan Using the SEIR-V Framework
<p><strong>Background:</strong> The COVID-19 pandemic posed significant challenges for public health systems globally, particularly in resource-limited settings such as Afghanistan. Limitations in diagnostic capacity, inconsistent data reporting, and low vaccination coverage hindered timely public health responses. To support real-time decision-making, accurate and adaptive modeling frameworks are essential.</p>
<p><strong>Methods:</strong> This study presents a hybrid modeling approach that integrates the classical SEIR-V (Susceptible–Exposed–Infectious–Recovered–Vaccinated) compartmental model with Dynamics-Informed Neural Networks (DINNs). The model embeds the SEIR-V system of differential equations into the loss function of a deep neural network to enable dynamic estimation of time-varying parameters. Epidemiological data from Feb 2020 to Apr 2024 were collected from multiple publicly available sources, including Worldometer, Our World in Data, the World Health Organization and the Johns Hopkins University COVID-19 repository.</p>
<p><strong>Results:</strong> The proposed DINNs-SEIRV model effectively reconstructed multiple epidemic waves and generated accurate forecasts of COVID-19 transmission dynamics in Afghanistan. The model achieved high predictive performance, particularly for the infectious (I) compartment, with a coefficient of determination R² = 0.9973. It also demonstrated strong capacity in capturing vaccination trends and maintaining robustness in the presence of incomplete or noisy data.</p>
<p><strong>Conclusion:</strong> The DINNs-SEIRV framework offers a powerful and flexible tool for modeling infectious disease dynamics in low-resource settings. Its ability to learn and update time-varying parameters in response to real-world data makes it valuable for informing public health strategy, forecasting outbreaks, and evaluating vaccination efforts in environments like Afghanistan</p>
https://creativecommons.org/licenses/by/4.0
Zabihullah Movaheedi, Ahmad Khosrow Sharifi, Nasiba Safi
2025
COVID-19
SEIR-V model
Dynamics-informed neural networks
Afghanistan
Epidemic modeling
Zabihullah
Movaheedi
Department of Mathematics, Faculty of Science, Herat University, Herat, Afghanistan
AF
z.movaheedi@hu.edu.af
Ahmad Khosrow
Sharifi
Department of Finance and Banking, Economics Faculty, Herat University, Herat, Afghanistan
AF
khosrow.sharifi7@gmail.com
Nasiba
Safi
Department of Mathematics, Faculty of Education, Herat University, Herat, Afghanistan
AF
nasibasafi@gmail.com
128
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Third Volume, second Issue, July 2025
177-192