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Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study

Open access
Bi, Qifang, Wu, Yongsheng, Mei, Shujiang, Ye, Chenfei, Zou, Xuan, Zhang, Zhen, Liu, Xiaojian, Wei, Lan, Truelove, Shaun A, Zhang, Tong, Gao, Wei, Cheng, Cong, Tang, Xiujuan, Wu, Xiaoliang, Wu, Yu, Sun, Binbin, Huang, Suli, Sun, Yu, Zhang, Juncen, Ma, Ting, Lessler, Justin, Feng, Tiejian·2020·The Lancet Infectious Diseases

Published version of a preprint

Abstract

Summary Background Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in Shenzhen, China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control measures. Methods From Jan 14 to Feb 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 SARS-CoV-2 cases and 1286 close contacts. We compared cases identified through symptomatic surveillance and contact tracing, and estimated the time from symptom onset to confirmation, isolation, and admission to hospital. We estimated metrics of disease transmission and analysed factors influencing transmission risk. Findings Cases were older than the general population (mean age 45 years) and balanced between males (n=187) and females (n=204). 356 (91%) of 391 cases had mild or moderate clinical severity at initial assessment. As of Feb 22, 2020, three cases had died and 225 had recovered (median time to recovery 21 days; 95% CI 20–22). Cases were isolated on average 4·6 days (95% CI 4·1–5·0) after developing symptoms; contact tracing reduced this by 1·9 days (95% CI 1·1–2·7). Household contacts and those travelling with a case were at higher risk of infection (odds ratio 6·27 [95% CI 1·49–26·33] for household contacts and 7·06 [1·43–34·91] for those travelling with a case) than other close contacts. The household secondary attack rate was 11·2% (95% CI 9·1–13·8), and children were as likely to be infected as adults (infection rate 7·4% in children <10 years vs population average of 6·6%). The observed reproductive number (R) was 0·4 (95% CI 0·3–0·5), with a mean serial interval of 6·3 days (95% CI 5·2–7·6). Interpretation Our data on cases as well as their infected and uninfected close contacts provide key insights into the epidemiology of SARS-CoV-2. This analysis shows that isolation and contact tracing reduce the time during which cases are infectious in the community, thereby reducing the R. The overall impact of isolation and contact tracing, however, is uncertain and highly dependent on the number of asymptomatic cases. Moreover, children are at a similar risk of infection to the general population, although less likely to have severe symptoms; hence they should be considered in analyses of transmission and control. Funding Emergency Response Program of Harbin Institute of Technology, Emergency Response Program of Peng Cheng Laboratory, US Centers for Disease Control and Prevention.

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Research works printed but not linked: 13

  1. Wang, 2020, Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China, JAMA, 323, 1061, 10.1001/jama.2020.1585 · DOI · Paper page
  2. Yang, 2020, Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China, MedRxiv
  3. Backer, 2020, Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20–28 January 2020, Euro Surveill, 25, 10.2807/1560-7917.ES.2020.25.5.2000062 · DOI · Paper page
  4. Chan, 2020, A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster, Lancet, 395, 514, 10.1016/S0140-6736(20)30154-9 · DOI · Paper page
  5. Lessler, 2009, Outbreak of 2009 pandemic influenza A (H1N1) at a New York City school, N Engl J Med, 361, 2628, 10.1056/NEJMoa0906089 · DOI · Paper page
  6. Lipsitch, 2003, Transmission dynamics and control of severe acute respiratory syndrome, Science, 300, 1966, 10.1126/science.1086616 · DOI · Paper page
  7. Lessler, 2016, Estimating the severity and subclinical burden of Middle East respiratory syndrome coronavirus infection in the Kingdom of Saudi Arabia, Am J Epidemiol, 183, 657, 10.1093/aje/kwv452 · DOI
  8. Reich, 2009, Estimating incubation period distributions with coarse data, Stat Med, 28, 2769, 10.1002/sim.3659 · DOI
  9. Reich, 2012, Estimating absolute and relative case fatality ratios from infectious disease surveillance data, Biometrics, 68, 598, 10.1111/j.1541-0420.2011.01709.x · DOI
  10. Peiris, 2003, Clinical progression and viral load in a community outbreak of coronavirus-associated SARS pneumonia: a prospective study, Lancet, 361, 1767, 10.1016/S0140-6736(03)13412-5 · DOI · Paper page
  11. Zou, 2020, SARS-CoV-2 viral load in upper respiratory specimens of infected patients, N Engl J Med, 382, 1177, 10.1056/NEJMc2001737 · DOI · Paper page
  12. Pan, 2020, Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia, Radiology, 10.1148/radiol.2020200370 · DOI · Paper page
  13. Fang, 2020, Sensitivity of chest CT for COVID-19: comparison to RT-PCR, Radiology, 10.1148/radiol.2020200432 · DOI · Paper page

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Metadata

Authors
Bi, Q., Wu, Y., Mei, S., Ye, C., Zou, X., Zhang, Z., Liu, X., Wei, L., Truelove, S. A., Zhang, T., Gao, W., Cheng, C., Tang, X., Wu, X., Wu, Y., Sun, B., Huang, S., Sun, Y., Zhang, J., Ma, T., Lessler, J., & Feng, T.
Venue
The Lancet Infectious Diseases
Publisher
Elsevier BV
Published
2020-08
Volume
20
Issue
8
Pages
911-919
ISSN
1473-3099 (ISSN-L)
OpenAlex
W3020184843
Cited by
1,958
References
5
FWCI
70.57
Type
journal article
Language
en
Update notices
erratum (2020-07-01)
Open access
bronze
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
  • Centers for Disease Control and Prevention (NU2GGH002000)
  • Harbin Institute of Technology (PCLERP001, HITERP010)

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