Estimating the serial interval of the novel coronavirus disease (COVID-19) : a statistical analysis using the public data in Hong Kong from january 16 to february 15, 2020

Abstract

Background: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) since the end of 2019. As of February 15, there were 56 COVID-19 cases confirmed in Hong Kong since the first case with symptom onset on January 23, 2020. Methods: Based on the publicly available surveillance data in Hong Kong, we identified 21 transmission events as of February 15, 2020. An interval censored likelihood framework is adopted to fit three different distributions including Gamma, Weibull, and lognormal, that govern the serial interval (SI) of COVID-19. We selected the distribution according to the Akaike information criterion corrected for small sample size (AICc). Findings: We found the lognormal distribution performed slightly better than the other two distributions in terms of the AICc. Assuming a lognormal distribution model, we estimated the mean of SI at 4.9 days (95% CI: 3.6-6.2) and SD of SI at 4.4 days (95% CI: 2.9-8.3) by using the information of all 21 transmission events. Conclusion: The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are crucially needed in combating the COVID-19 outbreak.