Meta analysis of the SARS CoV 2 serial interval and the impact of parameter uncertainty on the COVID 19 reproduction number
The serial interval of an infectious disease, commonly interpreted as the time between onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections) and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early COVID 19 data. In this paper we estimate these key quantities in the context of COVID 19 for the UK, including a meta analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean 5.6 (95% CrI 5.1 6.2) and SD 4.2 (95% CrI 3.9 4.6) days (empirical distribution) the generation interval with a mean 4.8 (95% CrI 4.3 5.41) and SD 1.7 (95% CrI 1.0 2.6) days (fitted gamma distribution) and the incubation period with a mean 5.5 (95% CrI 5.1 5.8) and SD 4.9 (95% CrI 4.5 5.3) days (fitted log normal distribution) We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modelling COVID 19 transmission.
medRxiv