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Commentary on the use of the reproduction number R during the COVID-19 pandemic.
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.
  • Vegvari C; Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, 4615Imperial College London, London, UK.
  • Abbott S; Center for the Mathematical Modelling of Infectious Diseases, 4906London School of Hygiene & Tropical Medicine, UK.
  • Ball F; School of Mathematical Sciences, 6123University of Nottingham, UK.
  • Brooks-Pollock E; Bristol Veterinary School, 1980University of Bristol, UK.
  • Challen R; NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol, UK.
  • Collyer BS; EPSRC Centre for Predictive Modelling in Healthcare, 3286University of Exeter, UK.
  • Dangerfield C; Somerset NHS Foundation Trust, UK.
  • Gog JR; Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, 4615Imperial College London, London, UK.
  • Gostic KM; 65899Isaac Newton Institute for Mathematical Sciences, UK.
  • Heffernan JM; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK.
  • Hollingsworth TD; Department of Ecology and Evolution, 2462University of Chicago, USA.
  • Isham V; Centre for Disease Modelling, Mathematics & Statistics, 7991York University, Canada.
  • Kenah E; COVID Modelling Task-Force, The Fields Institute, Canada.
  • Mollison D; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, 6396University of Oxford, UK.
  • Panovska-Griffiths J; Department of Statistical Science, 4919University College London, UK.
  • Pellis L; Division of Biostatistics, College of Public Health, 2647The Ohio State University, USA.
  • Roberts MG; Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK.
  • Scalia Tomba G; The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
  • Thompson RN; Wolfson Centre for Mathematical Biology, Mathematical Institute and The Queen's College, University of Oxford, Oxford, UK.
  • Trapman P; Department of Mathematics, 5292The University of Manchester, UK.
Stat Methods Med Res ; 31(9): 1675-1685, 2022 09.
Article in English | MEDLINE | ID: covidwho-2236610
ABSTRACT
Since the beginning of the COVID-19 pandemic, the reproduction number [Formula see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula see text] seems convenient, because the epidemic is expanding if [Formula see text] and contracting if [Formula see text]. The magnitude of [Formula see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula see text] in a naïve way can cause new problems. The reasons for this are threefold (1) There is not just one definition of [Formula see text] but many, and the precise definition of [Formula see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula see text], 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 [Formula see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula see text] 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 [Formula see text], and the data and methods used to estimate it, can make [Formula see text] a more useful metric for future management of the epidemic.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: Stat Methods Med Res Year: 2022 Document Type: Article Affiliation country: 09622802211037079

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: Stat Methods Med Res Year: 2022 Document Type: Article Affiliation country: 09622802211037079