Computational simulation demonstrates accurate dynamics for fractional prey-predator infection models using variational iteration, highlighting memory effects on population stability.
Key Points
Investigate the dynamical properties and memory effects of a fractional-order prey-predator infection model using the Fractional Variational Iteration Method (FVIM).
Simulated the fractional-order prey-predator infection system across varying fractional derivative orders using the FVIM.
Implemented a least-squares-based parameter identification algorithm combined with threshold analysis to estimate model parameters and dynamic responses.
Assessed numerical convergence and solution accuracy via iteration errors and discrete variable-wise errors.
FVIM produced precise analytical approximations that showed strong agreement with observed dynamic trajectories.
The least-squares optimization algorithm successfully recovered governing system parameters and reconstructed population trajectories.
Simulations across different fractional orders demonstrated that memory effects substantially alter population persistence and infection dynamics over time.