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Related Experiment Video

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A Primary Neuron Culture System for the Study of Herpes Simplex Virus Latency and Reactivation
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Memory-driven modeling of herpes simplex virus type-1 and type-2 dynamics with neural network optimization.

Zhang Nan1, Emmanuel Addai2, Ridwan Amure2

  • 1School of Mathematics and Statistics, Taiyuan Normal University, TaiYuan, Shanxi, China.

Computer Methods and Programs in Biomedicine
|October 22, 2025
PubMed
Summary

This study introduces a new mathematical model for Herpes Simplex Virus (HSV) types 1 and 2, incorporating memory effects to better understand infection dynamics and evaluate interventions like public awareness campaigns.

Keywords:
Artificial neural networksCaputo fractional derivativeFixed point theoremHSV1HVS2Hyers–Ulam stability

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Virology

Background:

  • Herpes Simplex Virus (HSV) types 1 and 2 are chronic infections with significant health impacts.
  • Traditional models struggle to capture the memory-dependent nature of HSV transmission.
  • A novel fractional-order modeling framework is developed to address these limitations.

Purpose of the Study:

  • To develop a fractional-order compartmental model for HSV-1 and HSV-2 transmission dynamics.
  • To incorporate memory effects into epidemiological models for chronic viral infections.
  • To evaluate the impact of interventions, such as public awareness, on HSV control.

Main Methods:

  • A fractional-order compartmental model using the Caputo derivative was constructed.
  • Qualitative properties and the basic reproduction number were analyzed.
  • Sensitivity analysis and numerical simulations were performed, alongside deep neural network implementation.

Main Results:

  • Fractional-order dynamics significantly impact infection persistence; lower orders prolong infectious periods.
  • Public awareness and transmission rates are key parameters influencing HSV spread.
  • Increased public awareness effectively reduces infection levels, as confirmed by simulations and a highly accurate neural network model.

Conclusions:

  • Fractional derivatives enhance model realism for HSV by capturing memory effects.
  • The integrated framework of fractional modeling and deep learning allows for accurate simulation and intervention assessment.
  • This approach provides a powerful tool for public health planning and optimizing HSV control strategies.