New strategy to control covid-19 pandemic using lead/lag compensator

Area of Science:

• Control Theory
• Epidemiology
• Mathematical Modeling

Background:

• The COVID-19 pandemic remains a significant global health challenge.
• Various strategies like suppression and mitigation have been employed to control its spread.
• Mathematical and engineering approaches are crucial for pandemic management.

Purpose of the Study:

• To propose a novel lead/lag compensator for controlling the nonlinear COVID-19 system.
• To utilize control theory principles to manage pandemic dynamics.
• To optimize controller parameters using the Most Valuable Player Algorithm (MVPA).

Main Methods:

• A lead/lag compensator was designed to address the unstable COVID-19 dynamics.
• The Most Valuable Player Algorithm (MVPA) was employed for parameter optimization.
• Simulations were conducted using real-world data from Hubei, China, and Lazio, Italy.

Main Results:

• The proposed lead/lag compensator demonstrated effective control over the COVID-19 system.
• Optimization via MVPA successfully tuned the controller parameters.
• Simulations validated the controller's performance using city-specific pandemic data.

Conclusions:

• The lead/lag compensator is a viable and effective tool for managing COVID-19 transmission.
• Control theory offers valuable methods for addressing complex epidemiological challenges.
• Data-driven simulations are essential for validating pandemic control strategies.