Fractional order modeling of hepatitis C transmission dynamics with physics-informed neural network solutions

February 24, 2026BMC Infectious DiseasesOpen Access

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Key Points

The study aims to develop a fractional-order model for understanding hepatitis C virus transmission dynamics and to utilize a physics-informed neural network for solutions.
Developed a six-compartment fractional-order model using the Caputo–Fabrizio derivative.
Conducted stability analyses to establish well-posedness of the model.
Performed sensitivity analysis to identify key drivers of new infections.
Implemented a Physics-Informed Neural Network to solve the fractional system numerically.
The basic reproduction number was found to be $$\mathfrak{R}_{0} = 2.697$$, indicating epidemic potential.
Acute transmission was identified as the predominant driver, contributing over 51% of new infections.
Fractional-order simulations indicated that lower orders delay infection waves, emphasizing the need for sustained interventions.
The PINN achieved high predictive accuracy for most compartments ($$R^{2} \geq 0.97$$) but performed moderately for chronic stages ($$R^{2} = 0.867$$).

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Develops a fractional-order model to analyze HCV transmission, suggesting strategies for public health interventions.

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Muthupandi et al. (2026) studied this question.

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