Analyzing the dynamic patterns of COVID-19 through nonstandard finite difference scheme

Area of Science:

• Epidemiology
• Computational Biology
• Numerical Analysis

Background:

• COVID-19 dynamics require accurate modeling for effective public health strategies.
• Traditional numerical methods can face stability and accuracy challenges in epidemic modeling.

Purpose of the Study:

• To develop and analyze a novel nonstandard finite difference (NSFD) system for modeling COVID-19.
• To incorporate asymptomatic and symptomatic individuals for a comprehensive epidemic analysis.
• To demonstrate the stability and efficiency advantages of NSFD schemes over traditional methods.

Main Methods:

• Development of an unconditionally stable NSFD system for COVID-19 dynamics.
• Inclusion of both asymptomatic and symptomatic infected individuals in the model.
• Rigorous numerical analysis and simulations to evaluate NSFD performance.
• Validation of analytical findings with numerical results.

Main Results:

• The proposed NSFD system demonstrates unconditional stability, eliminating the need for traditional methods like Runge-Kutta.
• Numerical simulations effectively capture the complex dynamics of COVID-19 spread.
• The NSFD approach offers improved stability and computational efficiency for infectious disease modeling.

Conclusions:

• NSFD schemes provide a robust and efficient tool for analyzing infectious disease dynamics, including COVID-19.
• The developed model offers a more comprehensive understanding of epidemic spread by including asymptomatic cases.
• This work highlights the potential of NSFD methods in advancing epidemiological research and public health preparedness.