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This study introduces a novel mathematical model for COVID-19, integrating within-host and between-host dynamics. The findings highlight that a combined intervention strategy, including immune support and preventative measures, is crucial for controlling viral spread.

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34A0892B0592C3792D30COVID-19Fixed pointFractional derivativesMulti-scaleSensitivity analysis

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Immunology

Background:

  • COVID-19 pandemics necessitate robust mathematical models for control.
  • Existing models often fail to integrate within-host and between-host disease dynamics.
  • Understanding the interplay between host immunity and transmission is critical.

Purpose of the Study:

  • To develop a novel mathematical model linking within-host and between-host factors of COVID-19.
  • To incorporate immune response dynamics into the epidemiological model.
  • To evaluate the efficacy of various intervention strategies.

Main Methods:

  • A compartmental mathematical model utilizing a fractional derivative (Caputo-Fabrizio) was developed.
  • Model properties were analyzed to ensure a unique solution.
  • Parameter estimation was performed using real-world data, followed by sensitivity analysis.
  • Numerical simulations were conducted to assess intervention impacts.

Main Results:

  • The model demonstrates that within-host dynamics significantly influence human-to-human transmission.
  • Sensitivity analysis identified key parameters affecting disease spread.
  • Simulations compared various intervention strategies based on averted deaths.

Conclusions:

  • No single intervention strategy is sufficient to control COVID-19 spread.
  • A combination of treatments boosting the immune system, inhibiting viral production, and adhering to public health protocols is essential.
  • The integrated model provides a framework for understanding and managing infectious disease dynamics.