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Genomic epidemiology offers high resolution estimates of serial intervals for COVID-19

Author

Listed:
  • Jessica E. Stockdale

    (Simon Fraser University)

  • Kurnia Susvitasari

    (Simon Fraser University)

  • Paul Tupper

    (Simon Fraser University)

  • Benjamin Sobkowiak

    (Simon Fraser University)

  • Nicola Mulberry

    (Simon Fraser University)

  • Anders Gonçalves da Silva

    (University of Melbourne at the Peter Doherty Institute for Infection & Immunity)

  • Anne E. Watt

    (University of Melbourne at the Peter Doherty Institute for Infection & Immunity)

  • Norelle L. Sherry

    (University of Melbourne at the Peter Doherty Institute for Infection & Immunity)

  • Corinna Minko

    (Victorian Department of Health)

  • Benjamin P. Howden

    (University of Melbourne at the Peter Doherty Institute for Infection & Immunity)

  • Courtney R. Lane

    (University of Melbourne at the Peter Doherty Institute for Infection & Immunity)

  • Caroline Colijn

    (Simon Fraser University)

Abstract

Serial intervals – the time between symptom onset in infector and infectee – are a fundamental quantity in infectious disease control. However, their estimation requires knowledge of individuals’ exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom and thereby estimate serial intervals. We apply our technique to SARS-CoV-2 sequences from case clusters in the first two COVID-19 waves in Victoria, Australia. We find that our approach offers high resolution, cluster-specific serial interval estimates that are comparable with those obtained from contact data, despite requiring no knowledge of who infected whom and relying on incompletely-sampled data. Compared to a published serial interval, cluster-specific serial intervals can vary estimates of the effective reproduction number by a factor of 2–3. We find that serial interval estimates in settings such as schools and meat processing/packing plants are shorter than those in healthcare facilities.

Suggested Citation

  • Jessica E. Stockdale & Kurnia Susvitasari & Paul Tupper & Benjamin Sobkowiak & Nicola Mulberry & Anders Gonçalves da Silva & Anne E. Watt & Norelle L. Sherry & Corinna Minko & Benjamin P. Howden & Cou, 2023. "Genomic epidemiology offers high resolution estimates of serial intervals for COVID-19," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40544-y
    DOI: 10.1038/s41467-023-40544-y
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    References listed on IDEAS

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    1. Gytis Dudas & Luiz Max Carvalho & Trevor Bedford & Andrew J. Tatem & Guy Baele & Nuno R. Faria & Daniel J. Park & Jason T. Ladner & Armando Arias & Danny Asogun & Filip Bielejec & Sarah L. Caddy & Mat, 2017. "Virus genomes reveal factors that spread and sustained the Ebola epidemic," Nature, Nature, vol. 544(7650), pages 309-315, April.
    2. Torsten Seemann & Courtney R. Lane & Norelle L. Sherry & Sebastian Duchene & Anders Gonçalves da Silva & Leon Caly & Michelle Sait & Susan A. Ballard & Kristy Horan & Mark B. Schultz & Tuyet Hoang & M, 2020. "Tracking the COVID-19 pandemic in Australia using genomics," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. Matthew Hall & Mark Woolhouse & Andrew Rambaut, 2015. "Epidemic Reconstruction in a Phylogenetics Framework: Transmission Trees as Partitions of the Node Set," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-36, December.
    4. Gregory F. Albery & Evan A. Eskew & Noam Ross & Kevin J. Olival, 2020. "Predicting the global mammalian viral sharing network using phylogeography," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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