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Modeling and control of COVID-19 disease using deep reinforcement learning method.

Nazanin Ghazizadeh1, Sajjad Taghvaei1, Seyyed Arash Haghpanah2

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Researchers developed a new SQEIAR model to control COVID-19 spread using optimal control strategies. The model significantly reduced deaths and symptomatic infections, demonstrating robustness against disturbances.

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Area of Science:

  • Epidemiology and Public Health
  • Computational Modeling and Control Theory

Background:

  • The COVID-19 pandemic has had devastating global health and economic impacts, necessitating effective disease control strategies.
  • Existing epidemiological models often lack the granularity to fully capture the complex dynamics of disease spread and intervention effectiveness.

Purpose of the Study:

  • To introduce and evaluate a novel SQEIAR epidemiological model for COVID-19.
  • To apply optimal control methods to minimize disease prevalence and intervention costs.
  • To assess the robustness of the proposed control strategy under various uncertainties.

Main Methods:

  • Developed the SQEIAR (Susceptible, Quarantined, Exposed, Infectious-Symptomatic, Infectious-Asymptomatic, Recovered) model incorporating six population groups.
  • Integrated three control inputs: quarantine of susceptible individuals, vaccination, and treatment.
  • Employed the Deep Deterministic Policy Gradient (DDPG) algorithm for optimal control to minimize symptomatic individuals and costs.
  • Simulated the model under different control scenarios and evaluated outcomes, including robustness testing with noise and parameter uncertainty.

Main Results:

  • The optimal control strategy reduced deaths by 60% and symptomatic infections by 74% compared to the uncontrolled model.
  • The DDPG algorithm successfully identified optimal control inputs for various intervention scenarios.
  • The control system demonstrated significant robustness when subjected to noise in observer variables, control inputs, and model parameters.

Conclusions:

  • The SQEIAR model with DDPG-based optimal control offers a powerful framework for managing infectious disease outbreaks like COVID-19.
  • The proposed intervention strategies are effective in mitigating disease spread and mortality.
  • The control system's robustness ensures reliable performance even in the presence of real-world uncertainties.