Commentary on the use of the reproduction number R during the COVID-19 pandemic. (September 2022)

• Record Type: Journal Article
• Authors: Vegvari, Carolin; Abbott, Sam; Ball, Frank; Brooks-Pollock, Ellen; Challen, Robert; Collyer, Benjamin S; Dangerfield, Ciara; Gog, Julia R; Gostic, Katelyn M; Heffernan, Jane M; Hollingsworth, T Déirdre; Isham, Valerie; Kenah, Eben; Mollison, Denis; Panovska-Griffiths, Jasmina; Pellis, Lorenzo; Roberts, Michael G; Scalia Tomba, Gianpaolo; Thompson, Robin N; Trapman, Pieter
• Other Names: De Angelis Daniela guest-editor.; Birrell Paul guest-editor.; Funk Sebastian guest-editor.; House Thomas guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 31:Number 9(2022)
• Page Start: 1675
• Page End: 1685
• Publication Date: 2022-09
• Subjects: Reproduction number -- COVID-19 pandemic; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802211037079
• Languages: English
• ISSNs: 0962-2802

Abstract

Since the beginning of the COVID-19 pandemic, the reproduction numberR 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 ifR > 1 and contracting ifR < 1 . The magnitude ofR indicates by how much transmission needs to be reduced to control the epidemic. UsingR in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition ofR but many, and the precise definition ofR affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly definedR, 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 estimateR vary, and it is not always clear what data should be included in the estimation. In this review, we discuss whenR 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 ofR, and the data and methods used to estimate it, can makeR a more useful metric for future management of the epidemic.