Vegvari, C;
Abbott, S;
Ball, F;
Brooks-Pollock, E;
Challen, R;
Collyer, BS;
Dangerfield, C;
... Trapman, P; + view all
(2021)
Commentary on the use of the reproduction number R during the COVID-19 pandemic.
Statistical Methods in Medical Research
10.1177/09622802211037079.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Commentary on the use of the reproduction number R during the COVID-19 pandemic |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1177/09622802211037079 |
| Publisher version: | https://doi.org/10.1177%2F09622802211037079 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Health Care Sciences & Services, Mathematical & Computational Biology, Medical Informatics, Statistics & Probability, Mathematics, Reproduction number, COVID-19 pandemic, EPIDEMIC MODELS, REAL-TIME, HOUSEHOLDS, IMMUNITY, VACCINATION, POPULATION, COMMUNITY, RATES |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10137550 |
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