Inference of the SARS-CoV-2 generation time using UK household data

  1. William Stephen Hart  Is a corresponding author
  2. Sam Abbott
  3. Akira Endo
  4. Joel Hellewell
  5. Elizabeth Miller
  6. Nick Andrews
  7. Philip K Maini
  8. Sebastian Funk
  9. Robin N Thompson
  1. University of Oxford, United Kingdom
  2. London School of Hygiene and Tropical Medicine, United Kingdom
  3. Public Health England, United Kingdom
  4. University of Warwick, United Kingdom

Abstract

The distribution of the generation time (the interval between individuals becoming infected and transmitting the virus) characterises changes in the transmission risk during SARS-CoV-2 infections. Inferring the generation time distribution is essential to plan and assess public health measures. We previously developed a mechanistic approach for estimating the generation time, which provided an improved fit to data from the early months of the COVID-19 pandemic (December 2019-March 2020) compared to existing models (Hart et al., 2021). However, few estimates of the generation time exist based on data from later in the pandemic. Here, using data from a household study conducted from March-November 2020 in the UK, we provide updated estimates of the generation time. We considered both a commonly used approach in which the transmission risk is assumed to be independent of when symptoms develop, and our mechanistic model in which transmission and symptoms are linked explicitly. Assuming independent transmission and symptoms, we estimated a mean generation time (4.2 days, 95% credible interval 3.3-5.3 days) similar to previous estimates from other countries, but with a higher standard deviation (4.9 days, 3.0-8.3 days). Using our mechanistic approach, we estimated a longer mean generation time (5.9 days, 5.2-7.0 days) and a similar standard deviation (4.8 days, 4.0-6.3 days). As well as estimating the generation time using data from the entire study period, we also considered whether the generation time varied temporally. Both models suggest a shorter mean generation time in September-November 2020 compared to earlier months. Since the SARS-CoV-2 generation time appears to be changing, further data collection and analysis is necessary to continue to monitor ongoing transmission and inform future public health policy decisions.

Data availability

All data generated or analysed during this study are included in the manuscript and its supporting files; a Source Data file has been provided for Figure 1. Code for reproducing our results is available at https://github.com/will-s-hart/UK-generation-times.

The following data sets were generated

Article and author information

Author details

  1. William Stephen Hart

    Mathematical Institute, University of Oxford, Oxford, United Kingdom
    For correspondence
    william.hart@keble.ox.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2504-6860
  2. Sam Abbott

    Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    No competing interests declared.
  3. Akira Endo

    Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    Akira Endo, received a research grant from Taisho Pharmaceutical Co., Ltd..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6377-7296
  4. Joel Hellewell

    Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    No competing interests declared.
  5. Elizabeth Miller

    Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1884-0097
  6. Nick Andrews

    Data and Analytical Sciences, UK Health Security Agency, Public Health England, London, United Kingdom
    Competing interests
    No competing interests declared.
  7. Philip K Maini

    Mathematical Institute, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0146-9164
  8. Sebastian Funk

    Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2842-3406
  9. Robin N Thompson

    Mathematics Institute, University of Warwick, Coventry, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8545-5212

Funding

Engineering and Physical Sciences Research Council (Excellence Award,EP/R513295/1)

  • William Stephen Hart

National Institute for Health Research (NIHR200929)

  • Elizabeth Miller

Taisho Pharmaceutical Co., Ltd (Research grant)

  • Akira Endo

UKRI (EP/V053507/1)

  • Robin N Thompson

The authors had sole responsibility for the study design, data collection, data analysis, data interpretation, and writing of the report.

Reviewing Editor

  1. Jennifer Flegg, The University of Melbourne, Australia

Version history

  1. Received: May 29, 2021
  2. Preprint posted: May 30, 2021 (view preprint)
  3. Accepted: February 7, 2022
  4. Accepted Manuscript published: February 9, 2022 (version 1)
  5. Version of Record published: March 30, 2022 (version 2)

Copyright

© 2022, Hart 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. William Stephen Hart
  2. Sam Abbott
  3. Akira Endo
  4. Joel Hellewell
  5. Elizabeth Miller
  6. Nick Andrews
  7. Philip K Maini
  8. Sebastian Funk
  9. Robin N Thompson
(2022)
Inference of the SARS-CoV-2 generation time using UK household data
eLife 11:e70767.
https://doi.org/10.7554/eLife.70767

Share this article

https://doi.org/10.7554/eLife.70767

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