A stochastic scale conjugate neural network procedure for the SIRC epidemic delay differential system
Zulqurnain Sabir1, Atef F Hashem2,3, Zill E Shams4
1Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
Summary
This study introduces a novel neural network approach for solving the SIRC epidemic delay differential model (SIRC-EDDM) related to COVID-19 dynamics. The scale conjugate gradient neural networks (SCGNNs) demonstrate high accuracy and efficiency in numerical simulations.
Area of Science:
- Epidemiology and Computational Mathematics
- Application of Artificial Intelligence in Disease Modeling
Background:
- The COVID-19 pandemic highlights the need for accurate mathematical models to understand disease spread.
- Delay differential models, such as the SIRC-EDDM, are crucial for capturing complex epidemic dynamics.
- Numerical solutions are essential for analyzing and predicting the behavior of these models.
Purpose of the Study:
- To develop and present a stochastic computing structure for the numerical solutions of the SIRC-EDDM.
- To investigate the efficacy of scale conjugate gradient neural networks (SCGNNs) for treating the SIRC-EDDM.
- To analyze the numerical performance of SCGNNs across different scenarios of the SIRC-EDDM.
Main Methods:
- Implementation of scale conjugate gradient neural networks (SCGNNs) for numerical solutions.
- Modeling the SIRC epidemic dynamics, including susceptible, infected, recovered, and cross-immune compartments.
- Validation using the Runge-Kutta scheme for comparison and assessment of absolute error (AE).
Main Results:
- SCGNNs achieved high accuracy with negligible absolute errors (10^-06 to 10^-08) across three different cases of SIRC-EDDM.
- Reduced mean square error (MSE) was observed using train, validation, and test data.
- Neuron analysis indicated that 14 neurons provided greater accuracy than 4 neurons.
Conclusions:
- SCGNNs offer a proficient and accurate method for the numerical treatment of the SIRC-EDDM.
- The study validates the effectiveness of SCGNNs through comprehensive error analysis, including MSE, regression, and correlation.
- The findings contribute to the advancement of computational methods for epidemic modeling, particularly for COVID-19 dynamics.
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