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The emerging virus, COVID-19, has caused a massive outbreak worldwide. Based on the publicly available contact-tracing data, we identified 509 transmission chains from 20 provinces in China and estimated the serial interval (SI) and generation interval (GI) of COVID-19 in China. Inspired by different possible 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 other transmissions among 2+. The corresponding SI (GI) is respectively denoted asSI1    0(GI1    0),SI2    1(GI2    1), andSI3  +    2  +(GI3  +    2  +). A Bayesian approach with doubly interval-censored likelihood is employed to fit the distribution function of the SI and GI. It was found that the estimatedSI1    0    =  6  .  52      (   95  %      CI   :  5  .  96  -  7  .  13   )    ,SI2    1    =  6  .  01      (   95  %   CI   :  5  .  44  -  6  .  64   )    ,SI3  +    2  +    =  4  .  39      (   95  %      CI   :  3  .  74  -  5  .  15   )    , andGI1    0    =  5  .  47      (   95  %      CI   :  4  .  57  -  6  .  45   )    ,GI2    1    =  5  .  01      (   95  %      CI   :  3  .  58  -  7  .  06   )    ,GI3  +    2  +    =  4  .  25      (   95  %      CI   :  2  .  82  -  6  .  23   )    . Thus, overall both SI and GI decrease when generation increases.

frontiers in public health

Serial Interval and Generation Interval for Imported and Local Infectors, Respectively, Estimated Using Reported Contact-Tracing Data of COVID-19 in China