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

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. Tsang, Peng Wu, Gabriel M. Leung and Benjamin J. Cowling ()
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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
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
Nancy H. L. Leung: The University of Hong Kong
Tim K. Tsang: The University of Hong Kong
Peng Wu: The University of Hong Kong
Gabriel M. Leung: The University of Hong Kong
Benjamin J. Cowling: The University of Hong Kong

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-28812-9

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