Computational analysis of stochastic delay dynamics in maize streak virus

Area of Science:

• Plant Pathology
• Computational Biology
• Epidemiology

Background:

• Maize Streak Virus (MSV) poses a significant threat to maize production globally.
• Understanding MSV transmission dynamics is crucial for effective disease management.

Purpose of the Study:

• To analyze MSV transmission dynamics using computational and stochastic modeling.
• To incorporate time delays and uncertainty factors into epidemic modeling.

Main Methods:

• Developed a compartmental MSV deterministic model extended to a stochastic delay differential system.
• Employed analytical methods for equilibrium analysis and derived the treatment reproduction number.
• Compared numerical methods including Euler-Maruyama, stochastic Runge-Kutta, and the stochastic Nonstandard Finite Difference (NSFD) scheme.

Main Results:

• Theoretical analysis confirmed the stability of equilibrium points under specific parameter values.
• The stochastic NSFD scheme demonstrated superior stability, positivity preservation, and step-size independence compared to classical methods.
• Incorporating stochasticity enhanced the validity of predictive simulations by capturing real-world transmission uncertainties.

Conclusions:

• The proposed stochastic NSFD model offers a computationally efficient and biologically realistic approach for simulating MSV and other plant virus epidemics.
• The findings enhance understanding and inform agricultural disease control strategies.
• This research provides a robust tool for managing plant virus outbreaks.