Commentary on the use of the reproduction number R during the COVID-19 pandemic

Carolin Vegvari, Sam Abbott, Frank Ball, Ellen Brooks-Pollock, Robert Challen, Benjamin S. Collyer, Ciara Dangerfield, Julia R. Gog, Katelyn M. Gostic, Jane M. Heffernan, T. Déirdre Hollingsworth, Valerie Isham, Eben Kenah, Denis Mollison, Jasmina Panovska-Griffiths, Lorenzo Pellis, Michael G. Roberts, Gianpaolo Scalia Tomba, Robin N. Thompson, Pieter Trapman

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Abstract

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.

Original languageEnglish
JournalStatistical Methods in Medical Research
Early online date27 Sep 2021
DOIs
Publication statusE-pub ahead of print - 27 Sep 2021

Keywords

  • COVID-19 pandemic
  • Reproduction number

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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