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Self-organized wavy infection curve of COVID-19

Takashi Odagaki1,2

  • 1Kyushu University, Fukuoka, 819-0395, Japan. t.odagaki@kb4.so-net.ne.jp.

Scientific Reports
|January 22, 2021
PubMed

Insights

Fluctuations in COVID-19 isolation policies in Japan created wavy infection curves. Self-organized waves emerged when the removal rate of infected individuals fell between two infection coefficient values.

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health Policy

Background:

  • The COVID-19 pandemic presented complex challenges in controlling viral spread.
  • Understanding the dynamics of infection curves is crucial for effective public health interventions.
  • Japan's COVID-19 trajectory exhibited unique wavy patterns that warranted investigation.

Purpose of the Study:

  • To investigate the causes behind the wavy COVID-19 infection curves observed in Japan.
  • To model the impact of fluctuating isolation policies on disease transmission.
  • To classify COVID-19 outbreak patterns based on epidemiological parameters.

Main Methods:

  • Utilized the Susceptible-Infected-Quarantined-Removed (SIQR) mathematical model.
  • Assumed the infection coefficient as a two-valued function dependent on daily new cases.
  • Analyzed the relationship between the removal rate and infection coefficient for self-organized wave emergence.

Main Results:

  • Demonstrated that fluctuating government and citizen adherence to isolation measures drives wavy infection curves.
  • Identified that self-organized wavy curves occur when the infected removal rate is between two specific infection coefficient values.
  • Classified COVID-19 outbreaks into five distinct types based on infection curve characteristics.

Conclusions:

  • Policy fluctuations and public compliance significantly influence COVID-19 transmission dynamics.
  • The interplay between transmission rates and quarantine effectiveness dictates outbreak patterns.
  • The study provides a framework for understanding and potentially managing diverse COVID-19 outbreak scenarios.

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