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Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission

Author

Listed:
  • Yun Lin

    (The University of Hong Kong)

  • Bingyi Yang

    (The University of Hong Kong)

  • Sarah Cobey

    (University of Chicago)

  • Eric H. Y. Lau

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

  • Dillon C. Adam

    (The University of Hong Kong)

  • Jessica Y. Wong

    (The University of Hong Kong)

  • Helen S. Bond

    (The University of Hong Kong)

  • Justin K. Cheung

    (The University of Hong Kong)

  • Faith Ho

    (The University of Hong Kong)

  • Huizhi Gao

    (The University of Hong Kong)

  • Sheikh Taslim Ali

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

  • Nancy H. L. Leung

    (The University of Hong Kong)

  • 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)

  • Gabriel M. Leung

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

  • Benjamin J. Cowling

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

Abstract

Many locations around the world have used real-time estimates of the time-varying effective reproductive number ( $${R}_{t}$$ R t ) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of $${R}_{t}$$ R t are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the latent period and reporting delays. Noting that viral loads tend to decline over time since illness onset, analysis of the distribution of viral loads among confirmed cases can provide insights into epidemic trajectory. Here, we analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by RT-qPCR cycle threshold values) and estimates of $${R}_{t}$$ R t based on case counts. We demonstrate that cycle threshold values could be used to improve real-time $${R}_{t}$$ R t estimation, enabling more timely tracking of epidemic dynamics.

Suggested Citation

  • Yun Lin & Bingyi Yang & Sarah Cobey & Eric H. Y. Lau & Dillon C. Adam & Jessica Y. Wong & Helen S. Bond & Justin K. Cheung & Faith Ho & Huizhi Gao & Sheikh Taslim Ali & Nancy H. L. Leung & Tim K. Tsan, 2022. "Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28812-9
    DOI: 10.1038/s41467-022-28812-9
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    References listed on IDEAS

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    1. Henrik Salje & Derek A. T. Cummings & Isabel Rodriguez-Barraquer & Leah C. Katzelnick & Justin Lessler & Chonticha Klungthong & Butsaya Thaisomboonsuk & Ananda Nisalak & Alden Weg & Damon Ellison & Lo, 2018. "Reconstruction of antibody dynamics and infection histories to evaluate dengue risk," Nature, Nature, vol. 557(7707), pages 719-723, May.
    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.
    3. Stephen M Kissler & Joseph R Fauver & Christina Mack & Scott W Olesen & Caroline Tai & Kristin Y Shiue & Chaney C Kalinich & Sarah Jednak & Isabel M Ott & Chantal B F Vogels & Jay Wohlgemuth & James W, 2021. "Viral dynamics of acute SARS-CoV-2 infection and applications to diagnostic and public health strategies," PLOS Biology, Public Library of Science, vol. 19(7), pages 1-17, July.
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