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

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作者
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
Sheikh Taslim Ali
机构
[1] The University of Hong Kong,WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine
[2] Hong Kong Science and Technology Park,Laboratory of Data Discovery for Health Limited
[3] New Territories,Department of Genetics
[4] College of Information and Communication Engineering,Department of Biomedical Data Sciences
[5] Dalian Minzu University,undefined
[6] University of Cambridge,undefined
[7] Center for Infectious Disease Control,undefined
[8] National Institute for Public Health and the Environment (RIVM),undefined
[9] Leiden University Medical Center,undefined
来源
Nature Communications | / 13卷
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摘要
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.
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