Serial interval and generation interval for respectively the imported and local infectors estimated using reported contact-tracing data of COVID-19 in China
Abstract
Backgrounds
The emerging virus, COVID-19, has caused a massive out-break worldwide. Based on the publicly available contact-tracing data, we identified 337 transmission chains from 10 provinces in China and estimated the serial interval (SI) and generation interval (GI) of COVID-19 in China.
Methods
Inspired by possibly different values of the time-varying reproduction number for the imported cases and the local cases in China, we divided all transmission events into three subsets: imported (the zeroth generation) infecting 1st-generation locals, 1st-generation locals infecting 2nd-generation locals, and others transmissions among 2+ generations. The corresponding SI (GI) is respec-tively denoted as <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20065946v1_inline1.gif"/> </alternatives> </inline-formula> , and <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20065946v1_inline2.gif"/> </alternatives> </inline-formula> . A Bayesian approach with doubly interval-censored likelihood is employed to fit the lognormal, gamma, and Weibull distribution function of the SI and GI using the identified 337 transmission chains.
Findings
It is found that the estimated <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20065946v1_inline3.gif"/> </alternatives> </inline-formula> , and <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20065946v1_inline4.gif"/> </alternatives> </inline-formula> , thus overall both SI and GI decrease when generation increases.
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