A novel coronavirus (SARS-CoV-2) emerged as a global threat in December 2019. As the epidemic progresses, disease modellers continue to focus on estimating the basic reproductive number
—the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates of during the beginning of the outbreak vary widely, despite relying on similar data sources. Here, we present a statistical framework for comparing and combining different estimates of across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate, the mean generation interval and the generation-interval dispersion. We apply our framework to early estimates of for the SARS-CoV-2 outbreak, showing that many estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of , including the shape of the generation-interval distribution, in efforts to estimate at the outset of an epidemic.