tetano
Editor, Senior Moderator
J R Soc Interface
. 2020 Jul;17(168):20200144.
doi: 10.1098/rsif.2020.0144. Epub 2020 Jul 22.
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[SUP] 1 [/SUP], Benjamin M Bolker[SUP] 2 3 4 [/SUP], David Champredon[SUP] 5 [/SUP], David J D Earn[SUP] 3 4 [/SUP], Michael Li[SUP] 2 [/SUP], Joshua S Weitz[SUP] 6 7 [/SUP], Bryan T Grenfell[SUP] 1 8 9 [/SUP], Jonathan Dushoff[SUP] 2 3 4 [/SUP]
Affiliations
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.
Keywords: Bayesian multilevel model; COVID-19; SARS-CoV-2; basic reproductive number; generation interval; novel coronavirus.
. 2020 Jul;17(168):20200144.
doi: 10.1098/rsif.2020.0144. Epub 2020 Jul 22.
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[SUP] 1 [/SUP], Benjamin M Bolker[SUP] 2 3 4 [/SUP], David Champredon[SUP] 5 [/SUP], David J D Earn[SUP] 3 4 [/SUP], Michael Li[SUP] 2 [/SUP], Joshua S Weitz[SUP] 6 7 [/SUP], Bryan T Grenfell[SUP] 1 8 9 [/SUP], Jonathan Dushoff[SUP] 2 3 4 [/SUP]
Affiliations
- PMID: 32693748
- DOI: 10.1098/rsif.2020.0144
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.
Keywords: Bayesian multilevel model; COVID-19; SARS-CoV-2; basic reproductive number; generation interval; novel coronavirus.