Estimating the time interval between transmission generations when negative values occur in the serial interval data: using COVID-19 as an example

Abstract

The coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in the end of 2019, and soon became a serious public health threat globally. Due to the unobservability, the time interval between transmission generations (TG), though important for understanding the disease transmission patterns, of COVID-19 cannot be directly summarized from surveillance data. In this study, we develop a likelihood framework to estimate the TG and the pre-symptomatic transmission period from the serial interval observations from the individual transmission events. As the results, we estimate the mean of TG at 4.0 days (95%CI: 3.3-4.6), and the mean of pre-symptomatic transmission period at 2.2 days (95%CI: 1.3-4.7). We approximate the mean latent period of 3.3 days, and 32.2% (95%CI: 10.3-73.7) of the secondary infections may be due to pre-symptomatic transmission. The timely and effectively isolation of symptomatic COVID-19 cases is crucial for mitigating the epidemics.

Keywords

• coronavirus disease 2019
• covid-19
• time of generation
• serial interval
• epidemic
• modelling