A Sequential Quadratic Programming Approach for the Predictive Control of the COVID-19 Spread

Area of Science:

• Epidemiology
• Control Systems Engineering
• Public Health

Background:

• The COVID-19 pandemic continues to pose significant global challenges, exacerbated by viral spread and emergent variants.
• High seroprevalence in populations has not prevented resurgent waves, highlighting the need for dynamic control strategies.
• Mass vaccination is not yet universally established, necessitating complementary public health interventions.

Purpose of the Study:

• To develop a novel Model Predictive Control (MPC) framework for managing the COVID-19 pandemic.
• To integrate social distancing guideline optimization with epidemiological forecasting.
• To provide a data-driven approach for mitigating viral transmission during vaccination.

Main Methods:

• A Linear Parameter Varying (LPV) version of the Susceptible-Infected-Recovered-Deceased (SIRD) model represents viral dynamics.
• The framework employs a Model Predictive Control (MPC) strategy for real-time decision-making.
• A Sequential Quadratic Program (SQP) algorithm is utilized to solve the LPV MPC problem and ensure convergent parameter estimation.

Main Results:

• The proposed LPV MPC framework effectively determines social distancing guidelines.
• The method provides accurate estimates of future epidemiological characteristics.
• Real-world data demonstrates the framework's efficiency in mitigating contagion alongside vaccination efforts.

Conclusions:

• The developed LPV MPC framework offers a robust tool for pandemic control.
• This approach enables adaptive social distancing strategies informed by epidemiological predictions.
• The study highlights the potential of advanced control systems in managing public health crises like COVID-19.