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A deep learning approach for solving a fractional order Monkeypox transmission model using a harmonic neural network
Nimra Shoket1, Abdul Mannan1, Jamshaid Ul Rahman1
1Abdus Salam School of Mathematical Sciences, Government College University Lahore, Lahore, 54600, Pakistan.
Scientific Reports
|May 26, 2026
Summary
This study uses a Harmonic neural network (HNN) with stochastic gradient descent with momentum (SGDM) to model Monkeypox transmission dynamics. The HNN-SGDM approach accurately captures complex disease patterns, offering a robust tool for epidemiological analysis.
Area of Science:
- Epidemiology
- Computational Biology
- Machine Learning
Background:
- Monkeypox transmission dynamics are complex and challenging to model using traditional methods.
- Harmonic neural networks (HNN) offer advantages in capturing oscillatory patterns common in disease spread.
- Stochastic gradient descent with momentum (SGDM) enhances optimization stability and convergence in complex models.
Purpose of the Study:
- To investigate Monkeypox transmission dynamics using a novel Harmonic neural network (HNN) framework.
- To optimize the HNN model using stochastic gradient descent with momentum (SGDM).
- To assess the precision, stability, and reliability of the proposed HNN-SGDM approach for epidemiological modeling.
Main Methods:
- Development and application of a Harmonic neural network (HNN) framework.
- Optimization of the HNN using stochastic gradient descent with momentum (SGDM).
- Modeling a nonlinear Monkeypox system with nine coupled differential equations representing human-rodent interactions.
Main Results:
- The HNN-SGDM solver achieved high precision, with absolute errors between [Formula: see text] and [Formula: see text].
- Numerical stability and convergence were confirmed through error analysis.
- Statistical performance measures (MAE, RMSE, Theil's inequality coefficient) and graphical analyses validated the model's robustness.
Conclusions:
- The HNN-SGDM framework demonstrates high accuracy and reliability for modeling Monkeypox transmission.
- Deep learning, particularly HNN, is effective for capturing complex, oscillatory dynamics in epidemiological modeling.
- The methodology is extendable to modeling other infectious diseases.
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