[Epidemiological characteristics of two local COVID-19 outbreaks caused by 2019-nCoV Omicron variant in Guangzhou, China]

Zhonghua Liu Xing Bing Xue Za Zhi. 2022 Nov 10;43(11):1705-1710. doi: 10.3760/cma.j.cn112338-20220523-00450.
[Article in Chinese]

Abstract

Objective: To understand the epidemiological characteristics of two local COVID-19 outbreaks caused by 2019-nCoV Omicron variant in Guangzhou, such as incubation period, serial interval, basic reproductive number (R0) and the influence of gathering places on R0, and provide evidence for the prevention and control of Omicron variant infection. Methods: The data of daily confirmed cases of Omicron variant infection from April 8 to May 8, 2022 in two COVID-19 outbreaks in Guangzhou were collected for model fitting. Weibull, Gamma and lognormal distribution were used to estimate incubation period and serial interval. Exponential growth method and the maximum likelihood estimation were used to estimate R0. Results: The median of incubation period was 2.94 (95%CI: 2.52-3.38) days and median of serial interval was 3.32 (95%CI: 2.89-3.81) days. The estimated R0 in small-size place was 4.40 (95%CI: 3.95-4.85), while the estimated R0 at airport was 11.35 (95%CI: 11.02-11.67). Conclusion: The incubation period of Omicron variant in two local COVID-19 outbreaks in Guangzhou is significantly shorter than that of delta variant. The higher the gathering degree in a place, the larger the R0. Due to its rapid transmission, COVID-19 epidemic is prone to occur. Therefore, the COVID-19 prevention and control strategy should be dynamically adjusted in time.

目的: 本研究旨在估计广州市2起由新型冠状病毒(新冠病毒)奥密克戎变异株(BA.2)引起的本地疫情的潜伏期、序列间隔和基本再生数(R0)等流行病学参数,探索不同场所聚集性对R0的影响,为奥密克戎变异株疫情防控提供科学依据。 方法: 收集2022年4-5月广州市2起新冠病毒奥密克戎变异株本地疫情病例数据,使用Weibull、Gamma和lognormal分布对奥密克戎变异株本地疫情的潜伏期、序列间隔分布进行估计,采用指数增长法和极大似然法估计R0结果: 两起疫情中位潜伏期为2.94(95%CI:2.52~3.38)d;中位序列间隔为3.32(95%CI:2.89~3.81)d。小型场所聚集性疫情R0为4.40(95%CI:3.95~4.85),机场聚集性疫情R0为11.35(95%CI:11.02~11.67)。 结论: 广州市2起由新冠病毒奥密克戎变异株引起的本地疫情潜伏期较德尔塔变异株明显缩短。场所聚集程度越高,R0越大,传播速度越快,易呈现暴发疫情,应及时调整防控策略。.

Publication types

  • English Abstract

MeSH terms

  • COVID-19* / epidemiology
  • China / epidemiology
  • Disease Outbreaks
  • Humans
  • SARS-CoV-2*

Supplementary concepts

  • SARS-CoV-2 variants