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Modeling typhoid dynamics using recurrent neural networks with Bayesian regularization.

Zulqurnain Sabir1, M A Abdelkawy2, Muhammad Athar Mehmood3

  • 1Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.

Computational Biology and Chemistry
|November 23, 2025
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Summary
This summary is machine-generated.

This study numerically investigates the typhoid epidemic model using artificial intelligence. Bayesian regularization neural networks accurately predict disease spread, offering a novel approach for epidemiological modeling.

Keywords:
Artificial intelligenceBayesian regularizationEpidemic typhoidMean square errorRecurrent neural network

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Artificial Intelligence

Background:

  • Typhoid fever remains a significant public health concern globally.
  • Mathematical models are crucial for understanding disease transmission dynamics.
  • Accurate numerical methods are needed to simulate and predict epidemic behavior.

Purpose of the Study:

  • To numerically investigate an epidemic typhoid model.
  • To apply stochastic artificial intelligence, specifically Bayesian regularization neural networks, for typhoid modeling.
  • To assess the model's precision and efficiency in simulating disease dynamics.

Main Methods:

  • A five-compartment typhoid model (susceptible, carrier, infected, recovery, bacterial) was developed.
  • The model was numerically solved using a Runge-Kutta procedure to generate a dataset.
  • Bayesian regularization neural networks were employed for training, testing (15%), and validation (10%) of the model, with 75% of the data used for training.
  • Performance was evaluated using achieved vs. reference outcomes and error metrics.

Main Results:

  • The model achieved high precision, with absolute errors ranging from 10^-06 to 10^-07.
  • Mean square error was significantly reduced, falling between 10^-08 and 10^-10.
  • Efficiency was confirmed through tests including histogram error, state transitions, and correlation indexes.

Conclusions:

  • Stochastic artificial intelligence-based recurrent neural networks, particularly using Bayesian regularization, provide a precise and efficient method for numerical investigation of epidemic typhoid models.
  • The developed model demonstrates a strong capability in simulating typhoid transmission dynamics.
  • This approach offers a promising tool for epidemiological research and public health interventions.