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.

1. World Health Organization 2020. Pneumonia of unknown cause—China. www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/ (accessed 30 January 2020).

2. The proximal origin of SARS-CoV-2

3. Temporal dynamics in viral shedding and transmissibility of COVID-19

4. World Health Organization 2020. Coronavirus disease 2019 (COVID-19) situation report – 112. www.who.int/docs/default-source/coronaviruse/situation-reports/20200511-covid-19-sitrep-112.pdf?sfvrsn=813f2669̇2 (accessed 11 May 2020).

5. Bedford T Neher R Hadfield J Hodcroft E Ilcisin M Müller N. 2020 Genomic analysis of nCoV spread. Situation report 2020-01-23. https://nextstrain.org/narratives/ncov/sit-rep/2020-01-23 (accessed 24 January 2020).

Cited by 83 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3