Investigating factors behind the outbreak of the 6th and the 7th waves of COVID-19 in Tokyo

TL;DR

This study analyzes the factors behind Tokyo's 6th and 7th COVID-19 waves using the Avrami equation, revealing how virus variants and social behaviors contributed to prolonged and large outbreaks.

Contribution

It applies the Avrami equation to model COVID-19 waves, linking virus variants and social factors to infection dynamics in Tokyo.

Findings

Main wave driven by Omicron BA.1 and New Year holidays

Side waves caused by Omicron BA.2 and holiday social interactions

Infectivity estimated from domain growth rate and rise time

Abstract

The 6th wave of COVID-19 in Tokyo continued for the longest period of infection (about 190 days from late Nov. 2021), and the 7th wave, which occurred in mid-May 2022, was the largest wave ever (cumulative 1.7 million people). In order to elucidate their factors, the infection wave was analyzed by using the Avrami equation. The main component of the 6th wave was formed by the coupling of increased human interaction due to the New Year holidays and the invasion of the new virus variant Omicron BA.1. After that, side waves were formed by the coupling of the invasion of the new virus variant Omicron BA.2 and the human interaction in the consecutive holidays in February, March, and May. These side waves caused the 6th wave not to converge for a long time. The outbreak of the main component of the 7th wave occurred by the coupling of the invasion of the new virus variant Omicron BA.5 and multiple social factors, followed by human interaction during the July holidays. Based on the results that the domain growth rate $K$ and the infection rise time $t_\mathrm{on}$ were almost independent of the initial susceptible $D_\mathrm{s}$, dense nucleation followed by a near growth model was deduced. The quantity $K \cdot t_\mathrm{on}$ was considered to represent the infectivity of the virus.