https://doi.org/10.1140/epjp/s13360-024-05965-8
Regular Article
Novel exploration of machine learning solutions with supervised neural structures for nonlinear cholera epidemic probabilistic model with quarantined impact
1
Department of Mathematics, University of Narowal, 51600, Narowal, Pakistan
2
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, 64002, Douliou, Yunlin, Taiwan, R.O.C.
3
Department of Mathematics, University of Gujrat, 50700, Jalalpur, Pakistan
4
AI Center, Yuan Ze University, 320, Taoyuan, Taiwan
Received:
5
December
2024
Accepted:
30
December
2024
Published online:
24
January
2025
Cholera is mainly spread by the ingestion of contaminated food or water, especially in areas where poor sanitation is prevalent. The bacteria responsible for cholera, Vibrio cholerae, are observed to multiply in environments lacking proper water treatment and sewage management systems. A novel exploration of machine learning solutions is presented in this paper, with supervised neural structures being applied to a nonlinear stochastic cholera epidemic (SCE) model that incorporates quarantined impact and Brownian motion uncertainty. Artificial neural networks optimized by the Levenberg–Marquardt algorithm (ANNs-LMA) are utilized to predict the dynamics of the SCE model. The probabilistic dynamics of the representative nonlinear SCE model are described in terms of susceptible, infected, quarantined, and recovered individuals, along with the bacterial population represented by the concentration of cholera bacteria in water and food sources. Synthetic data for the execution of ANNs-LMA are generated using the Euler–Maruyama numerical method, with variations in key parameters, including the migration rate into the susceptible group, the transmission rate of cholera through contaminated food and water, the rate at which immunity is lost, natural death rates, the disease progression, and mortality rates among infected individuals, and the recovery or severe disease progression rates among quarantined individuals. The effectiveness of the proposed ANNs-LMA approach is demonstrated by its close alignment with the reference numerical results of the SCE model, as indicated by an error value approaching zero, and is further validated through various assessment metrics, including mean square error-based convergence, adaptive governing parameters, error histograms, and autocorrelation analyses.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
