Dynamical analysis of a fractional Maize Streak Disease model with enhanced predictive capabilities
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new fractal-fractional model to understand Maize Streak Disease (MSD) transmission. The findings show that insecticides can effectively control MSD spread in maize crops.
Area Of Science
- Mathematical modeling of infectious diseases
- Agricultural entomology
- Fractional calculus applications
Background
- Maize Streak Disease (MSD), caused by Maize Streak Virus (MSV), is a major threat to global maize production.
- Transmission occurs via Cicadulina leafhoppers, impacting crop yields significantly.
Purpose Of The Study
- To develop and analyze a novel fractal-fractional Caputo derivative model for MSD transmission dynamics.
- To evaluate the efficacy of insecticide interventions in controlling MSD spread.
- To investigate the influence of fractal-fractional parameters on disease dynamics.
Main Methods
- A fractal-fractional Caputo derivative model incorporating susceptible, treated, exposed, infected, and recovered compartments.
- Fixed-point theory for establishing existence and uniqueness of solutions.
- Ulam-Hyers stability analysis.
- Fractional Adams-Bashforth method for numerical simulations.
- Deep neural network analysis for parameter-based modeling.
Main Results
- The fractal-fractional model provides a more nuanced understanding of MSD dynamics than integer-order models.
- Numerical simulations demonstrate the impact of fractal orders and dimensions on disease transmission.
- Insecticide interventions were shown to approximate effective control of MSD spread.
- Deep neural networks were utilized for detailed MSD model analysis.
Conclusions
- The fractal-fractional approach offers a powerful tool for analyzing complex plant disease dynamics like MSD.
- Insecticide strategies can be optimized based on insights from this modeling approach.
- Further research can leverage advanced mathematical and computational methods for disease management.
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