Model predictive control for optimal social distancing in a type SIR-switched model.

Area of Science:

• Epidemiology
• Control Theory
• Mathematical Modeling

Background:

• Social distancing measures are crucial for managing COVID-19 pandemics.
• Continued epidemic waves hinder the full return of economic and social activities.
• Existing models require dynamic characterization for effective intervention.

Purpose of the Study:

• To analyze SIR-type models under control actions, particularly for second waves.
• To develop an optimized non-pharmaceutical intervention strategy using control theory.
• To balance epidemic control with socio-economic recovery and healthcare system capacity.

Main Methods:

• Utilizing SIR-type epidemiological models.
• Applying control theory for dynamic characterization of interventions.
• Implementing a switching non-linear model predictive control strategy.

Main Results:

• The proposed control strategy effectively minimizes the final epidemic size.
• It also reduces the duration of social restrictions.
• The model ensures infected prevalence does not exceed healthcare system capacity.

Conclusions:

• A novel switching non-linear model predictive control offers an optimized approach to managing COVID-19.
• This strategy balances epidemic containment with socio-economic considerations.
• The findings support dynamic and adaptive non-pharmaceutical interventions.