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

Author:

Park Sang Woo1ORCID,Bolker Benjamin M.234ORCID,Champredon David5ORCID,Earn David J. D.34ORCID,Li Michael2ORCID,Weitz Joshua S.67ORCID,Grenfell Bryan T.189,Dushoff Jonathan234ORCID

Affiliation:

1. Department of Ecology and Evolutionary Biology, Princeton, NJ, USA

2. Department of Biology, McMaster University, Hamilton, Ontario, Canada

3. Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada

4. M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada

5. Department of Pathology and Laboratory Medicine, University of Western Ontario, London, Ontario, Canada

6. School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

7. School of Physics, Georgia Institute of Technology, Atlanta, GA, USA

8. Princeton School of Public and International Affairs, Princeton, NJ, USA

9. Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA

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 R 0 —the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates of R 0 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 R 0 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 R 0 for the SARS-CoV-2 outbreak, showing that many R 0 estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of R 0 , including the shape of the generation-interval distribution, in efforts to estimate R 0 at the outset of an epidemic.

Funder

Natural Sciences and Engineering Research Council of Canada

Canadian Institutes of Health Research

Publisher

The Royal Society

Subject

Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology

Reference51 articles.

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