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A mathematical model for SARS-CoV-2 in variable-order fractional derivative.

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This study introduces a mathematical model for coronavirus hospitalization, estimating the basic reproduction number and demonstrating model stability. Findings offer strategies for managing future outbreaks using optimal policies.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Infectious Disease Dynamics

Background:

  • The COVID-19 pandemic highlighted the need for robust mathematical models to understand and manage infectious disease outbreaks.
  • Real-world data from early pandemic waves are crucial for validating and refining these models.

Purpose of the Study:

  • To develop and analyze a mathematical model for coronavirus (COVID-19) incorporating hospitalization data.
  • To estimate key epidemiological parameters, such as the basic reproduction number (R0).
  • To explore strategies for effective outbreak management and disease control.

Main Methods:

  • Mathematical modeling of disease transmission with hospitalization.
  • Analysis of model stability under different conditions (infection-free states).
  • Parameter estimation using real-world case data (March 06, 2021 - April 30, 2021).
  • Introduction of a variable-order model and application of a genetic algorithm for control strategy optimization.

Main Results:

  • The mathematical model demonstrated stability in the absence of infection.
  • The basic reproduction number (R0) was estimated to be [specific value, if available, otherwise state 'estimated'].
  • Graphical representations of effective parameters for disease elimination were generated.
  • A genetic algorithm provided high-quality control strategies for outbreak management.

Conclusions:

  • The developed mathematical model accurately fits real-world COVID-19 data.
  • The study provides a framework for decision-makers to develop effective strategies for managing future pandemic waves.
  • Optimized control policies derived from the model can enhance outbreak management.