Estimating the serial interval of the novel coronavirus disease (COVID‐19) based on the public surveillance data in Shenzhen, China from January 19 to February 22, 2020

Wang, Kai; Zhao, Shi; Liao, Ying; Zhao, Tiantian; Wang, Xiaoyan; Zhang, Xueliang; Jiao, Haiyan; Li, Huling; Yin, Yi; Wang, Maggie H; Xiao, Li; Wang, Lei; He, Daihai

BACKGROUNDS: The novel coronavirus disease (COVID‐19) poses serious threat to global public health and economics. Serial interval (SI), time between the symptom onsets of a primary case and a second case, is a key epidemiological parameter. We estimated SI of COVID‐19 in Shenzhen, China based on 27 records of transmission chains. METHODS: 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 exponential growth to estimate the reproduction number. FINDINGS: Using a Weibull distribution, we estimated mean SI at 5.9 days (95%CI: 3.9−9.6) and a standard deviation (SD) at 4.8 days (95%CI: 3.1−10.1). Using a logistic growth model, we estimated the basic reproduction number in Shenzhen at 2.6 (95%CI: 2.4−2.8). CONCLUSION: The SI of COVID‐19 is relative shorter than that of SARS and MERS, other two beta coronavirus diseases, which suggests the iteration of the transmission was rapid. It is crucial to isolate close contacts promptly to control the spread of COVID‐19 effectively.