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Critical Factors of Infection Wavy Curve Oscillation of COVID-19 and Future Predictions in Japan | OMICS International| Abstract
ISSN: 2332-0877

Journal of Infectious Diseases & Therapy
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  • Short Communication   
  • J Infect Dis Ther,
  • DOI: 10.4172/2332-0877.1000526

Critical Factors of Infection Wavy Curve Oscillation of COVID-19 and Future Predictions in Japan

Tatsuaki Tsuruyama1,2,3,4*
1Department of Drug Discovery Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
2Depatment of Medicine, Tazuke Kofukai Medical Research Institute, Kitano Hospital, Osaka, Japan
3Department of Physics, Tohoku University, Sendai, Japan
4Department of Molecular Biosciences, Radiation Effects Research Foundation, Hiroshima, Japan
*Corresponding Author : Tatsuaki Tsuruyama, Department of Drug Discovery Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan, Email: tsuruyam@kuhp.kyoto-u.ac.jp

Received Date: Dec 26, 2022 / Published Date: Jan 27, 2023

Abstract

A novel model based on the macromolecule diffusion theory predicted the oscillation of the number of coronavirus disease 2019 infections. In this model, the diffusion coefficient, which depends on population density, determines the wavy oscillation frequency. The recovery rate of infected individuals determines the wavy amplitude. These two factors are critical factors beyond which the value of the infection phase abruptly changes, such as the phase transition. Therefore, we must ensure that the values remain within the critical values of social and medical preventive measures. The number of new infections was expected to peak around the 200-day serial intervals.

Keywords: Infection wavy curve oscillation; Nonlinear kinetics; Diffusion coefficient; Recovery rate

Citation: Tsuruyama T (2023) Critical Factors of Infection Wavy Curve Oscillation of COVID-19 and Future Predictions in Japan. J Infect Dis Ther 11: 526. Doi: 10.4172/2332-0877.1000526

Copyright: © 2023 Tsuruyama T. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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