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Is it Curbing-spread of SARS-CoV-2 Variants by Considering Non-linear Predictive Control?

Mohadeseh Najafi1, Hamidreza Mortazavy Beni2, Ashkan Heydarian3

  • 1Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran.

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Summary

This study models SARS-COV-2 (COVID-19) using fractional calculus and nonlinear model predictive control (NMPC). The NMPC effectively reduces prediction errors in susceptible populations, offering a new tool for epidemic control.

Keywords:
Mathematical modelSARS-COV-2a nonlinear model predictive controlfractional-order calculations

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Control Theory

Background:

  • The ongoing impact of SARS-COV-2 (COVID-19) necessitates advanced modeling techniques.
  • Traditional epidemic models may not fully capture the complexities and long-term dynamics of infectious diseases.
  • The influence of control measures like quarantine requires robust analytical frameworks.

Purpose of the Study:

  • To investigate SARS-COV-2 epidemic patterns using fractional-order mathematical modeling.
  • To evaluate the efficacy of a nonlinear model predictive controller (NMPC) for epidemic monitoring and control.
  • To incorporate the effects of quarantine within the fractional-order model.

Main Methods:

  • Development of a fractional-order mathematical model for SARS-COV-2 transmission dynamics.
  • Integration of a nonlinear model predictive controller (NMPC) for real-time monitoring and prediction.
  • Simulation analysis comparing the proposed NMPC with fractional-order optimal control.

Main Results:

  • The fractional-order model captures memory and hereditary properties, offering enhanced parameter adjustability.
  • The proposed NMPC demonstrated significantly lower mean squared error (~3.6e-04) in predicting susceptible individuals compared to fractional-order optimal control (47.4).
  • Simulations confirmed the NMPC's ability to anticipate and manage future epidemic conditions.

Conclusions:

  • Fractional-order modeling combined with NMPC provides a powerful approach for analyzing and controlling epidemics.
  • The developed NMPC is a promising tool for reducing prediction errors in susceptible populations during outbreaks.
  • The methodology is adaptable for application to other infectious disease models.