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

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 () and Sheikh Taslim Ali
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Dongxuan Chen: The University of Hong Kong
Yiu-Chung Lau: The University of Hong Kong
Xiao-Ke Xu: College of Information and Communication Engineering, Dalian Minzu University
Lin Wang: University of Cambridge
Zhanwei Du: The University of Hong Kong
Tim K. Tsang: The University of Hong Kong
Peng Wu: The University of Hong Kong
Eric H. Y. Lau: The University of Hong Kong
Jacco Wallinga: Center for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM)
Benjamin J. Cowling: The University of Hong Kong
Sheikh Taslim Ali: The University of Hong Kong

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-35496-8

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