Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak

• Published in: Journal of the Royal Society interface Vol. 17; no. 168; p. 20200144
• Main Authors: Park, Sang Woo, Bolker, Benjamin M, Champredon, David, Earn, David J D, Li, Michael, Weitz, Joshua S, Grenfell, Bryan T, Dushoff, Jonathan
• Format: Journal Article
• Language: English
• Published: England The Royal Society 01-07-2020
• Subjects: Basic Reproduction Number - statistics & numerical data; Bayes Theorem; Betacoronavirus; China - epidemiology; Coronavirus Infections - epidemiology; Coronavirus Infections - transmission; COVID-19; Disease Outbreaks - statistics & numerical data; Epidemics - statistics & numerical data; Humans; Life Sciences–Mathematics interface; Markov Chains; Models, Biological; Monte Carlo Method; Pandemics; Pneumonia, Viral - epidemiology; Pneumonia, Viral - transmission; Probability; SARS-CoV-2; Uncertainty; China; COVID-19; generation interval; Bayesian multilevel model; novel coronavirus; SARS-CoV-2; basic reproductive number
• ISSN: 1742-5689; 1742-5662
• DOI: 10.1098/rsif.2020.0144

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 [Formula: see text]-the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates of [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] for the SARS-CoV-2 outbreak, showing that many [Formula: see text] estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of [Formula: see text], including the shape of the generation-interval distribution, in efforts to estimate [Formula: see text] at the outset of an epidemic.