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

Marcelo M Morato1,2, Gulherme N G Dos Reis1, Julio E Normey-Rico1

  • 1Dept. de Automação e Sistemas (DAS), Univ. Fed. de Santa Catarina, Florianópolis-SC, Brazil.

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Summary
This summary is machine-generated.

This study introduces a new Model Predictive Control (MPC) framework to manage COVID-19 spread. The system optimizes social distancing guidelines and predicts future disease trends, aiding mitigation efforts during ongoing vaccination campaigns.

Keywords:
COVID-19Linear Parameter Varying SystemsModel Predictive Control

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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.