| Autor: |
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.; NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol, UK., Challen R; EPSRC Centre for Predictive Modelling in Healthcare, 3286University of Exeter, UK.; Somerset NHS Foundation Trust, UK., Collyer BS; Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, 4615Imperial College London, London, UK., Dangerfield C; 65899Isaac Newton Institute for Mathematical Sciences, UK., Gog JR; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK., Gostic KM; Department of Ecology and Evolution, 2462University of Chicago, USA., Heffernan JM; Centre for Disease Modelling, Mathematics & Statistics, 7991York University, Canada.; COVID Modelling Task-Force, The Fields Institute, Canada., Hollingsworth TD; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, 6396University of Oxford, UK., Isham V; Department of Statistical Science, 4919University College London, UK., Kenah E; Division of Biostatistics, College of Public Health, 2647The Ohio State University, USA., Mollison D; Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK., Panovska-Griffiths J; The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.; Wolfson Centre for Mathematical Biology, Mathematical Institute and The Queen's College, University of Oxford, Oxford, UK., Pellis L; Department of Mathematics, 5292The University of Manchester, UK.; The Alan Turing Institute, UK., Roberts MG; School of Natural and Computational Sciences and New Zealand Institute for Advanced Study, Massey University, New Zealand., Scalia Tomba G; Department of Mathematics, 9318University of Rome Tor Vergata, Italy., Thompson RN; Mathematics Institute, 2707University of Warwick, Coventry, UK.; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, 2707University of Warwick, Coventry, UK., Trapman P; Department of Mathematics, 7675Stockholm University, Sweden. |
| Abstrakt: |
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. |
