Dual ɛ-closed-loop Nash equilibrium method to study pandemic by numerical analysis.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Game Theory

Background:

• Coronavirus pandemics pose significant public health and economic challenges.
• Existing disease transmission models often lack comprehensive cost and vaccination dynamics.
• Understanding disease spread is crucial for effective intervention strategies.

Purpose of the Study:

• To present an advanced approach to modeling coronavirus pandemic transmission dynamics.
• To incorporate novel classes representing pandemic costs and individuals vaccinated without antibodies.
• To formulate conditions for a dual ɛ-closed-loop Nash equilibrium for disease control.

Main Methods:

• Development of a novel mathematical model for disease transmission.
• Inclusion of time-dependent parameters to reflect dynamic changes.
• Formulation of sufficient conditions for a dual ɛ-closed-loop Nash equilibrium using a verification theorem.

Main Results:

• A new model was constructed, enhancing existing disease transmission dynamics.
• Time-dependent parameters were integrated, allowing for more realistic scenario modeling.
• Sufficient conditions for Nash equilibrium were successfully formulated.

Conclusions:

• The developed model provides a more comprehensive framework for understanding coronavirus pandemic dynamics.
• The study offers a theoretical basis for optimizing disease control strategies through Nash equilibrium.
• The inclusion of costs and incomplete vaccination effects enhances the model's practical applicability.