โก Quick Summary
This study explores the dynamics of poliomyelitis transmission through innovative fractional-order models and deep neural networks (DNNs). The findings reveal that these models effectively capture the complexities of disease progression, particularly under vaccination and post-paralytic conditions.
๐ Key Details
- ๐ Models Used: Two fractional-order models with Atangana-Baleanu derivatives
- ๐งฉ Key Features: Incorporation of vaccination and post-paralytic population class
- โ๏ธ Technology: Deep Neural Networks for classification and forecasting
- ๐ Stability Analysis: Ulam-Hyers stability conducted through nonlinear techniques
๐ Key Takeaways
- ๐ Fractional-order modeling effectively captures memory and hereditary properties in disease dynamics.
- ๐ Deep Neural Networks enhance predictive capabilities for poliomyelitis spread.
- ๐งช Simulations show that all compartments achieve convergence and dynamic stability over time.
- โก Lower fractional orders lead to faster stabilization in disease dynamics.
- ๐ก Hybrid modeling integrates machine learning with traditional epidemiological approaches.
- ๐ Study conducted by researchers Al-Quran A, Shafqat R, Alsaadi A, and Djaouti AM.
- ๐ Published in: Sci Rep, 2025; 15:32023.
๐ Background
Poliomyelitis, a highly infectious viral disease, has been a significant public health concern. Traditional models of disease transmission often fail to account for the complexities of human behavior and vaccination effects. Recent advancements in mathematical modeling, particularly through the use of fractional-order derivatives, offer a promising avenue for more accurately simulating disease dynamics and improving public health responses.
๐๏ธ Study
The study presents a comprehensive analysis of poliomyelitis transmission dynamics using two fractional-order models that incorporate the Atangana-Baleanu derivatives in the Caputo sense. The researchers aimed to establish the existence and uniqueness of the model’s solution through fixed-point theory, while also conducting Ulam-Hyers stability analysis to assess robustness. The integration of DNN techniques further enhances the model’s predictive capabilities.
๐ Results
The simulations indicated that all compartments within the model achieved convergence and dynamic stability over time. Notably, lower fractional orders exhibited faster stabilization, highlighting the effectiveness of fractional modeling in capturing the complex behaviors of diseases. The DNN-based results closely aligned with numerical simulations, demonstrating high accuracy and validating the proposed hybrid modeling approach.
๐ Impact and Implications
This study represents a significant advancement in the field of infectious disease analysis. By integrating fractional-order modeling with machine learning, researchers can better understand and predict the spread of poliomyelitis, particularly in the context of vaccination and post-paralytic effects. Such innovative approaches could lead to more effective public health strategies and interventions, ultimately reducing the incidence of this debilitating disease.
๐ฎ Conclusion
The integration of fractional-order modeling and deep neural networks presents a powerful tool for analyzing infectious diseases like poliomyelitis. This study not only enhances our understanding of disease dynamics but also paves the way for future research in the field. As we continue to explore these innovative methodologies, the potential for improved public health outcomes becomes increasingly promising.
๐ฌ Your comments
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Poliomyelitis dynamics with fractional order derivatives and deep neural networks.
Abstract
This paper presents a comprehensive study of poliomyelitis transmission dynamics using two fractional-order models that incorporate the Atangana–Baleanu derivatives in the Caputo sense (ABC). The model includes critical epidemiological features, including vaccination and a post-paralytic population class. By utilizing the Mittag-Leffler kernel, the fractional framework captures memory and hereditary properties in disease progression. The existence and uniqueness of the model’s solution are established using fixed-point theory. To assess the model’s robustness, Ulam-Hyers stability analysis is conducted through nonlinear techniques. For numerical approximation, the iterative Adams-Bashforth scheme tailored for fractional orders is employed. Simulations are performed for a range of fractional orders and control strategies. The results indicate that all compartments achieve convergence and dynamic stability over time, with lower fractional orders exhibiting faster stabilization. These findings underscore the effectiveness of fractional modeling in capturing the complex behaviors of diseases. To enhance predictive capabilities, deep neural network (DNN) techniques are integrated into the framework. The dataset is partitioned into training, testing, and validation sets. The DNN is then used for classification, forecasting, and data-driven simulation of disease dynamics. The DNN-based results closely align with numerical simulations, demonstrating high accuracy and validating the proposed hybrid modeling approach. This study presents a novel integration of fractional-order modeling and machine learning for infectious disease analysis, providing a powerful tool for understanding and predicting poliomyelitis spread under vaccination and post-paralytic effects.
Author: [‘Al-Quran A’, ‘Shafqat R’, ‘Alsaadi A’, ‘Djaouti AM’]
Journal: Sci Rep
Citation: Al-Quran A, et al. Poliomyelitis dynamics with fractional order derivatives and deep neural networks. Poliomyelitis dynamics with fractional order derivatives and deep neural networks. 2025; 15:32023. doi: 10.1038/s41598-025-15195-2