Control of COVID-19 system using a novel nonlinear robust control algorithm

Area of Science:

• Epidemiology
• Control Theory
• Mathematical Modeling

Background:

• The COVID-19 pandemic necessitates innovative control strategies beyond traditional suppression and mitigation.
• Existing methods require complementary approaches to manage the epidemic's dynamics effectively.

Purpose of the Study:

• To introduce a new mathematical-engineering strategy for controlling the COVID-19 epidemic.
• To develop a robust control algorithm to compensate for the nonlinear dynamics of the COVID-19 system.

Main Methods:

• Application of control theory to manage epidemic instability.
• Utilization of Variable Transformation Technique (VTT) for system simplification.
• Optimization of controller parameters using the Most Valuable Player Algorithm (MVPA).

Main Results:

• The proposed robust control algorithm effectively compensates for the nonlinear COVID-19 system.
• Simulation results based on data from Hubei, China, and Lazio, Italy, demonstrate the algorithm's efficacy.
• The strategy proved capable of managing epidemic spread in real-world scenarios.

Conclusions:

• The developed mathematical-engineering strategy offers an effective method for COVID-19 control.
• This approach can be integrated with existing strategies to enhance epidemic management.
• The robust control algorithm provides a valuable tool for public health interventions during pandemics.