TY - JOUR
T1 - Commentary on the use of the reproduction number R during the COVID-19 pandemic
AU - Vegvari, Carolin
AU - Abbott, Sam
AU - Ball, Frank
AU - Brooks-Pollock, Ellen
AU - Challen, Robert
AU - Collyer, Benjamin S.
AU - Dangerfield, Ciara
AU - Gog, Julia R.
AU - Gostic, Katelyn M.
AU - Heffernan, Jane M.
AU - Hollingsworth, T. Déirdre
AU - Isham, Valerie
AU - Kenah, Eben
AU - Mollison, Denis
AU - Panovska-Griffiths, Jasmina
AU - Pellis, Lorenzo
AU - Roberts, Michael G.
AU - Scalia Tomba, Gianpaolo
AU - Thompson, Robin N.
AU - Trapman, Pieter
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: This work was supported by EPSRC grant no EP/R014604/1. EBP acknowledges funding from the Medical Research Council (MRC) (MC/PC/19067) and the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol. Support for RC’s research is provided by the EPSRC via grant EP/N014391/1, RC is also funded by the NHS Global Digital Exemplar programme (GDE). JMH acknowledges funding from the Natural Science and Engineering Research Council of Canada (NSERC), Canadian Institutes for Health Research (CIHR). MGR is supported by the Marsden Fund under contract MAU1718. GPST acknowledges the MIUR Excellence Department Project awarded to the Department of Mathematics, University of Rome Tor Vergata, CUP E83C18000100006. LP acknowledges the Wellcome Trust and the Royal Society (grant 202562/Z/16/Z) for funding. EK was supported by the National Institute of Allergy and Infectious Diseases (NIAID) grant R01 AI116770. PT acknowledges Vetenskapsrådet (Swedish Research Council), grant 2016-04566. KMG acknowledges fellowship support from the James S. McDonnell Foundation. The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIAID or the US National Institute of Health.
Funding Information:
The authors would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support and hospitality during the programme Infectious Dynamics of Pandemics where work on this paper was undertaken. This work was supported by EPSRC grant no EP/R014604/1. EBP acknowledges funding from the Medical Research Council.
Publisher Copyright:
© The Author(s) 2021.
PY - 2022/9
Y1 - 2022/9
N2 - Since the beginning of the COVID-19 pandemic, the reproduction number R has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, R is defined as the average number of secondary infections caused by one primary infected individual. R seems convenient, because the epidemic is expanding if R>1 and contracting if R<1. The magnitude of R indicates by how much transmission needs to be reduced to control the epidemic. Using R in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of R but many, and the precise definition of R affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined R, there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate R vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when R is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of R, and the data and methods used to estimate it, can make R a more useful metric for future management of the epidemic.
AB - Since the beginning of the COVID-19 pandemic, the reproduction number R has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, R is defined as the average number of secondary infections caused by one primary infected individual. R seems convenient, because the epidemic is expanding if R>1 and contracting if R<1. The magnitude of R indicates by how much transmission needs to be reduced to control the epidemic. Using R in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of R but many, and the precise definition of R affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined R, there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate R vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when R is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of R, and the data and methods used to estimate it, can make R a more useful metric for future management of the epidemic.
KW - COVID-19 pandemic
KW - Reproduction number
UR - http://www.scopus.com/inward/record.url?scp=85116061790&partnerID=8YFLogxK
U2 - 10.1177/09622802211037079
DO - 10.1177/09622802211037079
M3 - Article
C2 - 34569883
SN - 0962-2802
VL - 31
SP - 1675
EP - 1685
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 9
ER -