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Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19

Author

Listed:
  • Dongxuan Chen

    (The University of Hong Kong
    Hong Kong Science and Technology Park, New Territories)

  • Yiu-Chung Lau

    (The University of Hong Kong
    Hong Kong Science and Technology Park, New Territories)

  • Xiao-Ke Xu

    (College of Information and Communication Engineering, Dalian Minzu University)

  • Lin Wang

    (University of Cambridge)

  • Zhanwei Du

    (The University of Hong Kong
    Hong Kong Science and Technology Park, New Territories)

  • Tim K. Tsang

    (The University of Hong Kong
    Hong Kong Science and Technology Park, New Territories)

  • Peng Wu

    (The University of Hong Kong
    Hong Kong Science and Technology Park, New Territories)

  • Eric H. Y. Lau

    (The University of Hong Kong
    Hong Kong Science and Technology Park, New Territories)

  • Jacco Wallinga

    (Center for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM)
    Leiden University Medical Center)

  • Benjamin J. Cowling

    (The University of Hong Kong
    Hong Kong Science and Technology Park, New Territories)

  • Sheikh Taslim Ali

    (The University of Hong Kong
    Hong Kong Science and Technology Park, New Territories)

Abstract

The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.

Suggested Citation

  • Dongxuan Chen & Yiu-Chung Lau & Xiao-Ke Xu & Lin Wang & Zhanwei Du & Tim K. Tsang & Peng Wu & Eric H. Y. Lau & Jacco Wallinga & Benjamin J. Cowling & Sheikh Taslim Ali, 2022. "Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35496-8
    DOI: 10.1038/s41467-022-35496-8
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    References listed on IDEAS

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    1. Shengjie Lai & Nick W. Ruktanonchai & Liangcai Zhou & Olivia Prosper & Wei Luo & Jessica R. Floyd & Amy Wesolowski & Mauricio Santillana & Chi Zhang & Xiangjun Du & Hongjie Yu & Andrew J. Tatem, 2020. "Effect of non-pharmaceutical interventions to contain COVID-19 in China," Nature, Nature, vol. 585(7825), pages 410-413, September.
    2. Katelyn M Gostic & Lauren McGough & Edward B Baskerville & Sam Abbott & Keya Joshi & Christine Tedijanto & Rebecca Kahn & Rene Niehus & James A Hay & Pablo M De Salazar & Joel Hellewell & Sophie Meaki, 2020. "Practical considerations for measuring the effective reproductive number, Rt," PLOS Computational Biology, Public Library of Science, vol. 16(12), pages 1-21, December.
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