Abstract

A key unknown for SARS-CoV-2 is how asymptomatic infections contribute to transmission. We used a transmission model with asymptomatic and presymptomatic states, calibrated to data on disease onset and test frequency from the Diamond Princess cruise ship outbreak, to quantify the contribution of asymptomatic infections to transmission. The model estimated that 74% (70-78%, 95% posterior interval) of infections proceeded asymptomatically. Despite intense testing, 53% (51-56%) of infections remained undetected, most of them asymptomatic. Asymptomatic individuals were the source for 69% (20-85%) of all infections. The data did not allow identification of the infectiousness of asymptomatic infections, however low ranges (0-25%) required a net reproduction number for individuals progressing through presymptomatic and symptomatic stages of at least 15. Asymptomatic SARS-CoV-2 infections may contribute substantially to transmission. Control measures, and models projecting their potential impact, need to look beyond the symptomatic cases if they are to understand and address ongoing transmission.

Data availability

All data analysed during this study are included in the manuscript and supporting files. Model code is available through github.

Article and author information

Author details

  1. Jon C Emery

    Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Timothy W Russell

    Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Yang Liu

    Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Joel Hellewell

    Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Carl AB Pearson

    Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. CMMID COVID-19 Working Group

  7. Gwenan M Knight

    IDE, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7263-9896
  8. Rosalind M Eggo

    Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  9. Adam J Kucharski

    Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8814-9421
  10. Sebastian Funk

    Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2842-3406
  11. Stefan Flasche

    Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  12. Rein M G J Houben

    Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
    For correspondence
    rein.houben@lshtm.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4132-7467

Funding

European Research Council Starting Grant (Action Number 757699)

  • Jon C Emery
  • Rein M G J Houben

Wellcome (206250/Z/17/Z)

  • Timothy W Russell
  • Adam J Kucharski

Wellcome (208812/Z/17/Z)

  • Stefan Flasche

Wellcome (210758/Z/18/Z)

  • Joel Hellewell
  • Sebastian Funk

Bill and Melinda Gates Foundation (INV-003174)

  • Yang Liu

Bill and Melinda Gates Foundation (NTD Modelling Consortium OPP1184344)

  • Carl AB Pearson

DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z)

  • Carl AB Pearson

European Union Horizon 2020 (project EpiPose (101003688))

  • Yang Liu

HDR UK (MR/S003975/1)

  • Rosalind M Eggo

National Institute for Health Research (16/137/109)

  • Yang Liu

Medical Research Council (MC_PC 19065)

  • Rosalind M Eggo

Medical Research Council (MR/P014658/1)

  • Gwenan M Knight

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2020, Emery et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 6,522
    views
  • 639
    downloads
  • 58
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jon C Emery
  2. Timothy W Russell
  3. Yang Liu
  4. Joel Hellewell
  5. Carl AB Pearson
  6. CMMID COVID-19 Working Group
  7. Gwenan M Knight
  8. Rosalind M Eggo
  9. Adam J Kucharski
  10. Sebastian Funk
  11. Stefan Flasche
  12. Rein M G J Houben
(2020)
The contribution of asymptomatic SARS-CoV-2 infections to transmission on the Diamond Princess cruise ship
eLife 9:e58699.
https://doi.org/10.7554/eLife.58699

Share this article

https://doi.org/10.7554/eLife.58699

Further reading

    1. Medicine
    2. Microbiology and Infectious Disease
    3. Epidemiology and Global Health
    4. Immunology and Inflammation
    Edited by Jos WM van der Meer et al.
    Collection

    eLife has published articles on a wide range of infectious diseases, including COVID-19, influenza, tuberculosis, HIV/AIDS, malaria and typhoid fever.

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    Bo Zheng, Bronner P Gonçalves ... Caoyi Xue
    Research Article

    Background:

    In many settings, a large fraction of the population has both been vaccinated against and infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, quantifying the protection provided by post-infection vaccination has become critical for policy. We aimed to estimate the protective effect against SARS-CoV-2 reinfection of an additional vaccine dose after an initial Omicron variant infection.

    Methods:

    We report a retrospective, population-based cohort study performed in Shanghai, China, using electronic databases with information on SARS-CoV-2 infections and vaccination history. We compared reinfection incidence by post-infection vaccination status in individuals initially infected during the April–May 2022 Omicron variant surge in Shanghai and who had been vaccinated before that period. Cox models were fit to estimate adjusted hazard ratios (aHRs).

    Results:

    275,896 individuals were diagnosed with real-time polymerase chain reaction-confirmed SARS-CoV-2 infection in April–May 2022; 199,312/275,896 were included in analyses on the effect of a post-infection vaccine dose. Post-infection vaccination provided protection against reinfection (aHR 0.82; 95% confidence interval 0.79–0.85). For patients who had received one, two, or three vaccine doses before their first infection, hazard ratios for the post-infection vaccination effect were 0.84 (0.76–0.93), 0.87 (0.83–0.90), and 0.96 (0.74–1.23), respectively. Post-infection vaccination within 30 and 90 days before the second Omicron wave provided different degrees of protection (in aHR): 0.51 (0.44–0.58) and 0.67 (0.61–0.74), respectively. Moreover, for all vaccine types, but to different extents, a post-infection dose given to individuals who were fully vaccinated before first infection was protective.

    Conclusions:

    In previously vaccinated and infected individuals, an additional vaccine dose provided protection against Omicron variant reinfection. These observations will inform future policy decisions on COVID-19 vaccination in China and other countries.

    Funding:

    This study was funded the Key Discipline Program of Pudong New Area Health System (PWZxk2022-25), the Development and Application of Intelligent Epidemic Surveillance and AI Analysis System (21002411400), the Shanghai Public Health System Construction (GWVI-11.2-XD08), the Shanghai Health Commission Key Disciplines (GWVI-11.1-02), the Shanghai Health Commission Clinical Research Program (20214Y0020), the Shanghai Natural Science Foundation (22ZR1414600), and the Shanghai Young Health Talents Program (2022YQ076).