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

Li, Menghui; Liu, Kai; Song, Yukun; Wang, Ming; Wu, Jinshan

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 as [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]), and [Formula: see text] ([Formula: see text]). 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 estimated [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] , [Formula: see text] , [Formula: see text]. Thus, overall both SI and GI decrease when generation increases.