Serial interval and generation interval for respectively the imported and local infectors estimated using reported contact-tracing data of COVID-19 in China

ABSTRACT

BackgroundsThe 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. MethodsInspired 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 [Formula], and [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. FindingsIt is found that the estimated [Formula], and [Formula], thus overall both SI and GI decrease when generation increases.