https://doi.org/10.1140/epjp/s13360-025-06891-z
Regular Article
A radial basis Bayesian regularization procedure for the Lassa virus mathematical model
1
Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
2
Department of Computer Engineering, Biruni University, 34010, Istanbul, Turkey
Received:
2
September
2025
Accepted:
22
September
2025
Published online:
30
September
2025
The purpose of current research investigations is to perform the numerical investigations of the Lassa virus model by using the computing stochastic paradigms. The Lassa virus was identified first in Nigeria, and 20 years later, the mathematical Lassa virus model has been developed by including different factors such as population of human to human, rodent to human, and environmental influences. A single hidden layer neural network structure using a radial basis function, fifteen neurons, and optimization with Bayesian regularization is presented to solve the Lassa virus mathematical model. The construction of the dataset is performed by the explicit Runge–Kutta scheme, which reduces mean square error by dividing the statistics into training as 74%, while 14% for authentication and 12% for testing. The correctness of proposed scheme is authenticated by solving three different model cases including comparison of the results that are 6–8 decimal places, best training performances around 10−11–10−14, and absolute errors found as 10−06–10−08. The reliability of the designed stochastic neural network is obtained by using different tests including correlation, state transitions, and error histograms.
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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.
