1. Epidemiology and Global Health
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Evidence for transmission of COVID-19 prior to symptom onset

  1. Lauren C Tindale
  2. Jessica E Stockdale
  3. Michelle Coombe
  4. Emma S Garlock
  5. Wing Yin Venus Lau
  6. Manu Saraswat
  7. Louxin Zhang
  8. Dongxuan Chen
  9. Jacco Wallinga
  10. Caroline Colijn  Is a corresponding author
  1. University of British Columbia, Canada
  2. Simon Fraser University, Canada
  3. National University of Singapore, Singapore
  4. National Institute for Public Health and the Environment, Netherlands
  5. Leiden University Medical Center, Netherlands
Research Article
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Cite this article as: eLife 2020;9:e57149 doi: 10.7554/eLife.57149

Abstract

We collated contact tracing data from COVID-19 clusters in Singapore and Tianjin, China and estimated the extent of pre-symptomatic transmission by estimating incubation periods and serial intervals. The mean incubation periods accounting for intermediate cases were 4.91 days (95%CI 4.35, 5.69) and 7.54 (95%CI 6.76, 8.56) days for Singapore and Tianjin, respectively. The mean serial interval was 4.17 (95%CI 2.44, 5.89) and 4.31 (95%CI 2.91, 5.72) days (Singapore, Tianjin). The serial intervals are shorter than incubation periods, suggesting that pre-symptomatic transmission may occur in a large proportion of transmission events (0.4-0.5 in Singapore and 0.6-0.8 in Tianjin, in our analysis with intermediate cases, and more without intermediates). Given the evidence for pre-symptomatic transmission it is vital that even individuals who appear healthy abide by public health measures to control COVID-19.

Data availability

Data are available on github at github.com/CSSEGISandData/COVID-19. Code to produce all analyses is also available there. Source data files of the Singapore and Tianjin clusters have been provided.

Article and author information

Author details

  1. Lauren C Tindale

    Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
    Competing interests
    No competing interests declared.
  2. Jessica E Stockdale

    Mathematics, Simon Fraser University, Burnaby, Canada
    Competing interests
    No competing interests declared.
  3. Michelle Coombe

    School of Population and Public Health, University of British Columbia, Vancouver, Canada
    Competing interests
    No competing interests declared.
  4. Emma S Garlock

    Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada
    Competing interests
    No competing interests declared.
  5. Wing Yin Venus Lau

    Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada
    Competing interests
    No competing interests declared.
  6. Manu Saraswat

    Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, Canada
    Competing interests
    No competing interests declared.
  7. Louxin Zhang

    Department of Mathematics, National University of Singapore, Singapore, Singapore
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0260-824X
  8. Dongxuan Chen

    Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands
    Competing interests
    No competing interests declared.
  9. Jacco Wallinga

    Faculteit Geneeskunde, Leiden University Medical Center, Leiden, Netherlands
    Competing interests
    Jacco Wallinga, Reviewing editor, eLife.
  10. Caroline Colijn

    Mathematics, Simon Fraser University, Burnaby, Canada
    For correspondence
    ccolijn@sfu.ca
    Competing interests
    Caroline Colijn, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6097-6708

Funding

Government of Canada (Canada 150 Research Chair program)

  • Caroline Colijn

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

Reviewing Editor

  1. Marc Lipsitch, Harvard TH Chan School of Public Health, United States

Publication history

  1. Received: March 23, 2020
  2. Accepted: June 21, 2020
  3. Accepted Manuscript published: June 22, 2020 (version 1)
  4. Version of Record published: July 28, 2020 (version 2)

Copyright

© 2020, Tindale 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.

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  1. Further reading

Further reading

    1. Epidemiology and Global Health
    Andria Mousa et al.
    Research Article

    Background: Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focussed on high-income settings.

    Methods: Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys we explored how contact characteristics (number, location, duration and whether physical) vary across income settings.

    Results: Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age-groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, with low-income settings characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income-strata on the frequency, duration and type of contacts individuals made.

    Conclusions: These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens, as well as the effectiveness of different non-pharmaceutical interventions.

    Funding: This work is primarily being funded by joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1).

    1. Epidemiology and Global Health
    2. Medicine
    ISARIC Clinical Characterisation Group et al.
    Research Article

    Background: There is potentially considerable variation in the nature and duration of the care provided to hospitalised patients during an infectious disease epidemic or pandemic. Improvements in care and clinician confidence may shorten the time spent as an inpatient, or the need for admission to an intensive care unit (ICU) or high density unit (HDU). On the other hand, limited resources at times of high demand may lead to rationing. Nevertheless, these variables may be used as static proxies for disease severity, as outcome measures for trials, and to inform planning and logistics.

    Methods: We investigate these time trends in an extremely large international cohort of 142,540 patients hospitalised with COVID-19. Investigated are: time from symptom onset to hospital admission, probability of ICU/HDU admission, time from hospital admission to ICU/HDU admission, hospital case fatality ratio (hCFR) and total length of hospital stay.

    Results: Time from onset to admission showed a rapid decline during the first months of the pandemic followed by peaks during August/September and December 2020. ICU/HDU admission was more frequent from June to August. The hCFR was lowest from June to August. Raw numbers for overall hospital stay showed little variation, but there is clear decline in time to discharge for ICU/HDU survivors.

    Conclusions: Our results establish that variables of these kinds have limitations when used as outcome measures in a rapidly-evolving situation.

    Funding: This work was supported by the UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z] and the Bill and Melinda Gates Foundation [OPP1209135]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.