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Nonlinear model of infection wavy oscillation of COVID-19 in Japan based on diffusion kinetics

Tatsuaki Tsuruyama1,2,3,4

  • 1Department of Physics, Graduate School of Science, Tohoku University, Sendai, 980-8578, Japan. tsuruyam@kuhp.kyoto-u.ac.jp.

Scientific Reports
|November 10, 2022
PubMed

Insights

This study presents a new nonlinear mathematical model for SARS-CoV-2 infection dynamics. The model accurately simulates wavy oscillations in infection numbers, linking frequency and amplitude to recovery rates.

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Modeling

Background:

  • The global spread of SARS-CoV-2 continues, with Japan experiencing severe conditions.
  • Current control measures focus on medical resource management and activity restrictions.
  • Observed long-term infection data in Japan exhibits unique wavy oscillations, lacking comprehensive explanation.

Purpose of the Study:

  • To develop a novel nonlinear mathematical model explaining the unique wavy oscillations of SARS-CoV-2 infection.
  • To investigate the underlying mechanisms driving these infection patterns.

Main Methods:

  • Application of macromolecule diffusion theory to model infection propagation.
  • Introduction of a diffusion coefficient dependent on population density to introduce nonlinearity.
  • Simulation and analysis of the developed mathematical model.

Main Results:

  • The model accurately simulated the observed wavy oscillations in infection numbers.
  • A strong correlation was found between infection oscillation frequency/amplitude and the recovery rate of infected individuals.
  • The model successfully captured the nonlinear dynamics of infection spread.

Conclusions:

  • The developed nonlinear mathematical model offers a new framework for analyzing contact-based infections.
  • Understanding the relationship between recovery rates and oscillation patterns is crucial for infection control.
  • This approach provides insights into the complex dynamics of infectious disease propagation.

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