Estimating the serial interval of the novel coronavirus disease (COVID‐19) based on the public surveillance data in Shenzhen, China, from 19 January to 22 February 2020
Kai Wang, Shi Zhao, Ying Liao, Tiantian Zhao, Xiaoyan Wang, Xueliang Zhang, Jiao Haiyan, Huling Li, Yi Yin, Maggie Haitian Wang, Xiao Li, Lei Wang, Daihai He
Abstract
The novel coronavirus disease (COVID-19) poses a serious threat to global public health and economics. Serial interval (SI), time between the onset of symptoms of a primary case and a secondary case, is a key epidemiological parameter. We estimated SI of COVID-19 in Shenzhen, China based on 27 records of transmission chains. We adopted three parametric models: Weibull, lognormal and gamma distributions, and an interval-censored likelihood framework. The three models were compared using the corrected Akaike information criterion (AICc). We also fitted the epidemic curve of COVID-19 to the logistic growth model to estimate the reproduction number. Using a Weibull distribution, we estimated the mean SI to be 5.9 days (95% CI: 3.9-9.6) with a standard deviation (SD) of 4.8 days (95% CI: 3.1-10.1). Using a logistic growth model, we estimated the basic reproduction number in Shenzhen to be 2.6 (95% CI: 2.4-2.8). The SI of COVID-19 is relatively shorter than that of SARS and MERS, the other two betacoronavirus diseases, which suggests the iteration of the transmission may be rapid. Thus, it is crucial to isolate close contacts promptly to effectively control the spread of COVID-19.