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

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, Courtney R. Lane and Caroline Colijn
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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

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-40544-y

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