Covid-19 and Flattening the Curve: A Feedback Control Perspective.

Area of Science:

• Epidemiology
• Control Theory
• Public Health Policy

Background:

• COVID-19 pandemic necessitated public health interventions to manage infection rates.
• Policies aimed to "flatten the curve" by preventing healthcare system overload.
• Effective strategies are crucial for mitigating pandemic impact.

Purpose of the Study:

• To engineer a control theory-based strategy for flattening the COVID-19 infection curve.
• To develop an optimal control solution for managing pandemic peaks.
• To ensure near-optimal public health performance under uncertain conditions.

Main Methods:

• Formulation of the optimal flattening problem using control theory.
• Derivation of a closed-form solution for the optimal strategy.
• Augmentation with nonlinear closed-loop tracking for robust performance.

Main Results:

• A novel control strategy for flattening the COVID-19 curve was developed.
• The method demonstrated effectiveness in simulations under uncertain conditions.
• Validation included realistic scenarios, focusing on Codogno, Italy.

Conclusions:

• Control theory provides a viable framework for managing pandemic infection dynamics.
• The proposed strategy offers a robust approach to maintaining low infection peaks.
• The method has practical implications for future pandemic response planning.