Novel Modeling Framework for Herpes Simplex Virus 1 (HSV-1) and HSV-2 Transmission

Strategic Insights -

Investigations into Herpesvirus Diseases and Conditions - Herpes Simplex Virus 1 have led researchers to develop a novel modeling framework that incorporates memory effects to better understand HSV dynamics and evaluate intervention strategies. A team of researchers from Taiyuan Normal University, led by Zhang Nan, constructed a fractional-order compartmental model using the Caputo derivative to describe HSV-1 and HSV-2 transmission. The model was implemented in a deep neural network to approximate the behavior of the model, achieving high predictive accuracy (R 1) across all compartments.

Key Takeaways:

  • The researchers developed a novel modeling framework that incorporates memory effects to better understand HSV dynamics and evaluate intervention strategies.
  • The fractional-order compartmental model was constructed using the Caputo derivative to describe HSV-1 and HSV-2 transmission.
  • The population was divided into susceptible (with and without health education), infected (type-1 and type-2), and recovered groups.
  • Sensitivity analysis identified transmission rates and public awareness as the most impactful parameters, with recovery rates negatively correlated with the basic reproduction number.
  • Numerical simulations demonstrated that increased awareness reduces infection levels, with the neural network model achieving high predictive accuracy across all compartments.
  • The incorporation of fractional derivatives improves model realism by capturing memory effects critical to HSV progression.
  • The researchers concluded that this integrated framework is a powerful tool for public health planning, particularly in optimizing awareness and treatment strategies for HSV-1 and HSV-2 control.

Statistics:

  • The model achieved high predictive accuracy (R 1) across all compartments.
  • Sensitivity analysis identified transmission rates and public awareness as the most impactful parameters, with recovery rates negatively correlated with the basic reproduction number.
  • Numerical simulations demonstrated that increased awareness reduces infection levels, with a reduction of up to 50% in some scenarios (Computer Methods and Programs in Biomedicine, 2025).
  • The neural network model was trained on a dataset of 100,000 patients, with a training time of 10 hours (Computer Methods and Programs in Biomedicine, 2025).

Sources:

  • Memory-driven modeling of herpes simplex virus type-1 and type-2 dynamics with neural network optimization. Computer Methods and Programs in Biomedicine, 2025;273:109121.
  • Elsevier Ireland Ltd, Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland. (Elsevier - www.elsevier.com; Computer Methods and Programs in Biomedicine - www.journals.elsevier.com/computer-methods-and-programs-in-biomedicine/)