A comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing...

Area of Science:

• Epidemiology
• Computational Biology
• Mathematical Modeling

Background:

• COVID-19 presents complex dynamics influenced by numerous factors.
• Accurate modeling is crucial for effective public health interventions.
• Stochasticity in epidemic data introduces significant uncertainty.

Purpose of the Study:

• To comparatively analyze deterministic and stochastic computational models for optimal COVID-19 control.
• To develop and validate a stochastic compartmental epidemic model incorporating white noise.
• To assess the efficacy of control strategies under both deterministic and stochastic frameworks.

Main Methods:

• Formulation of a compartmental epidemic model with white noise perturbation.
• Establishment of mathematical properties: well-posedness and stationary distributions.
• Application of optimal control strategies in both deterministic and stochastic scenarios.
• Parameterization using reported COVID-19 data from Algeria.
• Numerical simulations to evaluate control measure effectiveness.

Main Results:

• The stochastic model effectively accounts for inherent uncertainties in epidemic data.
• Mathematical properties of the model ensure reliability for long-term dynamic analysis.
• Numerical simulations demonstrate varying effectiveness of control measures based on model type.
• The study provides insights into optimal control strategies for COVID-19 mitigation.

Conclusions:

• Stochastic modeling offers a more realistic approach to understanding and controlling COVID-19.
• The developed model and control strategies can inform public health decision-making.
• Comparative analysis highlights the importance of accounting for uncertainty in epidemic management.
• This research advances the understanding of epidemic dynamics and intervention effectiveness.