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Neuro-Evolutionary Computing Paradigm for the SIR Model Based on Infection Spread and Treatment

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Abstract

The intension of the present study is to solve the nonlinear biological susceptible, infected and recovered (SIR) models using Feed-Forward Artificial Neural Networks (FFANN) optimized with global search of genetic algorithm aided with rapid local search interior-point IP algorithms, i.e., FEANN-GAIP. An error-based cost function is formulated by exploiting FEANN models of differential equations and its associated conditions representing the SIR systems. The proposed FEANN-GAIP scheme is evaluated for three types of infection spread biological systems based on simple SIR, modified SIR and treatment-based SIR models. The reliability and correctness of the FEANN-GAIP scheme is substantiated with good agreement based on the results of the Adam numerical solver. The statistical outcomes further demonstrate the consistent precision, applicability, and robustness of the designed FEANN-GAIP based stochastic numerical solver.

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Acknowledgements

The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the General Research Funding program grant code (NU/RG/SERC/11/3).

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Authors and Affiliations

  1. CONACyT – Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca,, MOR, Mexico

    J. F. Gómez-Aguilar

  2. Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan

    Zulqurnain Sabir & Muhammad Umar

  3. Department of Mathematics, College of Sciences and Arts, Najran University, Najran, Kingdom of Saudi Arabia

    Manal Alqhtani & Khaled M. Saad

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  1. J. F. Gómez-Aguilar
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  2. Zulqurnain Sabir
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  3. Manal Alqhtani
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  4. Muhammad Umar
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  5. Khaled M. Saad
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Contributions

JFGA: Conceptualization, methodology, validation, writing-review and editing; ZS: Conceptualization, methodology, validation, Writing-original draft preparation, writing-review; MA: Conceptualization, methodology, validation, investigation, Writing-original draft preparation, writing-review; MU: Formal analysis, investigation, conceptualization, methodology; KMS: Validation, formal analysis, investigation, conceptualization, methodology. All authors have read and agreed to the published version of the manuscript.

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Correspondence to J. F. Gómez-Aguilar.

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Gómez-Aguilar, J.F., Sabir, Z., Alqhtani, M. et al. Neuro-Evolutionary Computing Paradigm for the SIR Model Based on Infection Spread and Treatment. Neural Process Lett 55, 4365–4396 (2023). https://doi.org/10.1007/s11063-022-11045-8

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