Key result
Fractional-order neural network modeling of HSV dynamics links increased public awareness to reduced infection levels.
Why the study?
Traditional epidemiological models often fail to capture the memory-dependent dynamics inherent in chronic infections like HSV-1 and HSV-2.
A fractional-order compartmental model combined with deep learning accurately simulates HSV-1 and HSV-2 dynamics, highlighting the importance of memory effects and public awareness in infection control.
Herpes Simplex Virus Types 1 and 2 (HSV-1 and HSV-2) are chronic viral infections with widespread prevalence and lasting health effects, including neurological and oncological complications. Traditional epidemiological models often fail to capture memory-dependent dynamics inherent in such infections. This study develops a novel modeling framework that incorporates memory effects to better understand HSV dynamics and evaluate intervention strategies. Methods: We constructed a fractional-order compartmental model using the Caputo derivative to describe HSV-1 and HSV-2 transmission. The population is divided into susceptible (with and without health education), infected (type-1 and type-2), and recovered groups. We examined the model’s qualitative properties, including existence, uniqueness, and stability. The basic reproduction number was derived, and sensitivity analysis was performed using the Latin Hypercube Sampling–Partial Rank Correlation Coefficient method. Numerical simulations were conducted via the Adams–Bashforth–Moulton predictor–corrector scheme. Additionally, a deep neural network was implemented to approximate the model’s behavior. Our findings show that fractional-order dynamics substantially influence infection persistence, with lower fractional orders prolonging infectious periods. Sensitivity analysis identified transmission rates and public awareness as the most impactful parameters. Recovery rates were negatively correlated with the basic reproduction number. Simulations demonstrated that increased awareness reduces infection levels. The neural network achieved high predictive accuracy across all compartments, effectively modeling both peak and recovery phases. The incorporation of fractional derivatives improves model realism by capturing memory effects critical to HSV progression. Combining this with deep learning enables accurate simulation and real-time assessment of interventions. 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.
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Human Sciences Research Council (2026) studied Herpes Simplex Virus Types 1 and 2 (HSV-1 and HSV-2). Fractional-order compartmental modeling and deep neural network was evaluated on Infection persistence and basic reproduction number. A fractional-order compartmental model combined with a deep neural network accurately simulated HSV-1 and HSV-2 dynamics, showing that increased public awareness reduces infection levels.
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