Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Dec:29:100592.
doi: 10.1016/j.lanwpc.2022.100592. Epub 2022 Sep 7.

Epidemiological characteristics and transmission dynamics of the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai, China: A descriptive study

Zhiyuan Chen  1 , Xiaowei Deng  1 , Liqun Fang  2 , Kaiyuan Sun  3 , Yanpeng Wu  1 , Tianle Che  2 , Junyi Zou  1 , Jun Cai  1 , Hengcong Liu  1 , Yan Wang  1 , Tao Wang  2 , Yuyang Tian  1 , Nan Zheng  1 , Xuemei Yan  1 , Ruijia Sun  1 , Xiangyanyu Xu  1 , Xiaoyu Zhou  1 , Shijia Ge  4 , Yuxia Liang  1 , Lan Yi  1 , Juan Yang  1   5 , Juanjuan Zhang  1 , Marco Ajelli  6 , Hongjie Yu  1   5
Affiliations

Epidemiological characteristics and transmission dynamics of the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai, China: A descriptive study

Zhiyuan Chen et al. Lancet Reg Health West Pac. 2022 Dec.

Abstract

Background: In early March 2022, a major outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant spread rapidly throughout Shanghai, China. Here we aimed to provide a description of the epidemiological characteristics and spatiotemporal transmission dynamics of the Omicron outbreak under the population-based screening and lockdown policies implemented in Shanghai.

Methods: We extracted individual information on SARS-CoV-2 infections reported between January 1 and May 31, 2022, and on the timeline of the adopted non-pharmaceutical interventions. The epidemic was divided into three phases: i) sporadic infections (January 1-February 28), ii) local transmission (March 1-March 31), and iii) city-wide lockdown (April 1 to May 31). We described the epidemic spread during these three phases and the subdistrict-level spatiotemporal distribution of the infections. To evaluate the impact on the transmission of SARS-CoV-2 of the adopted targeted interventions in Phase 2 and city-wide lockdown in Phase 3, we estimated the dynamics of the net reproduction number (Rt ).

Findings: A surge in imported infections in Phase 1 triggered cryptic local transmission of the Omicron variant in early March, resulting in the largest outbreak in mainland China since the original wave. A total of 626,000 SARS-CoV-2 infections were reported in 99.5% (215/216) of the subdistricts of Shanghai until the end of May. The spatial distribution of the infections was highly heterogeneous, with 37% of the subdistricts accounting for 80% of all infections. A clear trend from the city center towards adjacent suburban and rural areas was observed, with a progressive slowdown of the epidemic spread (from 463 to 244 meters/day) prior to the citywide lockdown. During Phase 2, Rt remained well above 1 despite the implementation of multiple targeted interventions. The citywide lockdown imposed on April 1 led to a marked decrease in transmission, bringing Rt below the epidemic threshold in the entire city on April 14 and ultimately leading to containment of the outbreak.

Interpretation: Our results highlight the risk of widespread outbreaks in mainland China, particularly under the heightened pressure of imported infections. The targeted interventions adopted in March 2022 were not capable of halting transmission, and the implementation of a strict, prolonged city-wide lockdown was needed to successfully contain the outbreak, highlighting the challenges for containing Omicron outbreaks.

Funding: Key Program of the National Natural Science Foundation of China (82130093); Shanghai Rising-Star Program (22QA1402300).

Keywords: Non-pharmaceutical intervention; Omicron; SARS-CoV-2; Shanghai outbreak; Transmission dynamics.

PubMed Disclaimer

Conflict of interest statement

H.Y. has received research funding from Sanofi Pasteur, GlaxoSmithKline, Yichang HEC Changjiang Pharmaceutical Company, Shanghai Roche Pharmaceutical Company, and SINOVAC Biotech Ltd. M.A. has received research funding from Seqirus. None of those research funding is related to this work. All other authors report no competing interests.

Figures

Figure 1
Figure 1
Timeline of the public health response in Shanghai by epidemic phase.
Figure 2
Figure 2
Temporal dynamics of local and imported SARS-CoV-2 infections in Shanghai since early 2020. (a) Number of reported SARS-CoV-2 infections in Shanghai between 2020 and 2022, stratified by local and imported infections. (b) The same as in (a), but for the period from January 1 to May 31, 2022.
Figure 3
Figure 3
Geographical distribution of SARS-CoV-2 infections. (a-d) Cumulative number of new SARS-CoV-2 infections per 1000 individuals in each phase and overall.
Figure 4
Figure 4
Spatial trends and speed of spread of the epidemic in the three phases. (a) Spatial location of the reported infections during the first phase of the epidemic. (b-c) Estimated arrival time of the epidemic in the different areas of Shanghai in Phase 2 and 3. Estimates are based on the thin spline regression of the interval between the time of the detection of the first infection in each 3 km × 3 km grid and February 27, 2022. Triangles indicate the potential source of the outbreak. (d) Estimated speed of spread of SARS-CoV-2 (left axis) and cumulative fraction of affected areas of Shanghai (right axis). Red dots indicate the speed of spread over time in each cell. The blue line indicates the average speed per day as obtained using a polynomial regression. Central areas contain the districts of Jing'an, Yangpu, Hongkou, Putuo, Changning, Xuhui, and Huangpu.
Figure 5
Figure 5
Characterization of the epidemic dynamics between March 16 and March 29, 2022. (a) Location of high-risk, moderate-risk, and low-risk areas. For each area, its highest risk classification was used. (b-d) Number of reported infections per 1000 individuals between March 16 and March 29 by area type. (e) Number of new reported infection per 1000 individuals by area type and time. (f) Estimated Rt between March 16 and March 29 by area type. (g) Estimated epidemic growth rate and doubling time (days).
Figure 6
Figure 6
Epidemic dynamics under the effect of interventions. (a) Number of new SARS-CoV-2 infections by date of sample collection, stratified by means of identification. (b) Estimated Rt (mean and 50% confidence interval) in eastern, western, and all Shanghai areas.
See this image and copyright information in PMC

Update of

References

    1. Pan A, Liu L, Wang C, et al. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. Jama. 2020;323(19):1915–1923. - PMC - PubMed
    1. Luo L, Yang Z, Liang J, et al. Crucial control measures to contain China's first Delta variant outbreak. Natl Sci Rev. 2022;9(4):nwac004. - PMC - PubMed
    1. Li L, Han ZG, Qin PZ, et al. Transmission and containment of the SARS-CoV-2 Delta variant of concern in Guangzhou, China: A population-based study. PLoS Negl Trop Dis. 2022;16(1) - PMC - PubMed
    1. World Health Organization. Tracking SARS-CoV-2 variants. 2022. - PubMed
    1. Viana R, Moyo S, Amoako DG, et al. Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa. Nature. 2022;603(7902):679–686. - PMC - PubMed