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

Sang Woo Park, Benjamin M. Bolker, David Champredon, David J.D. Earn, Michael Li, Joshua S. Weitz, Bryan T. Grenfell, Jonathan Dushoff

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)20200144
Number of pages1
JournalJournal of the Royal Society, Interface
Volume17
Issue number168
DOIs
StatePublished - Jul 1 2020

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biophysics
  • Biochemistry
  • Biotechnology
  • Biomedical Engineering
  • Biomaterials

Keywords

  • Bayesian multilevel model
  • COVID-19
  • SARS-CoV-2
  • basic reproductive number
  • generation interval
  • novel coronavirus

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