Open AccessReconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak
Author(s) -
Sang Woo Park,
Benjamin M. Bolker,
David Champredon,
David J. D. Earn,
Michael Li,
Joshua S. Weitz,
Bryan T. Grenfell,
Jonathan Dushoff
Publication year - 2020
Publication title -
journal of the royal society interface
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 139
eISSN - 1742-5689
pISSN - 1742-5662
DOI - 10.1098/rsif.2020.0144
Subject(s) - algorithm , computer science , artificial intelligence , machine learning
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 numberR0—the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates ofR0during 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 ofR0across 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 ofR0for the SARS-CoV-2 outbreak, showing that manyR0estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components ofR0, including the shape of the generation-interval distribution, in efforts to estimateR0at the outset of an epidemic.