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Estimation of reproduction numbers in real time : conceptual and statistical challenges
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Pellis, Lorenzo, Birrell, Paul J., Blake, Joshua, Overton, Christopher E., Scarabel, Francesca, Stage, Helena B., Brooks‐Pollock, Ellen, Danon, Leon, Hall, Ian, House, Thomas A., Keeling, Matt J., Read, Jonathan M. and De Angelis, Daniela (2022) Estimation of reproduction numbers in real time : conceptual and statistical challenges. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185 (S1). pp. 112-130. doi:10.1111/rssa.12955 ISSN 0964-1998.
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WRAP-estimation-reproduction-numbers-real-time-conceptual-statistical-challenges-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2038Kb) | Preview |
Official URL: https://doi.org/10.1111/rssa.12955
Abstract
The reproduction number R has been a central metric of the COVID-19 pandemic response, published weekly by the UK government and regularly reported in the media. Here, we provide a formal definition and discuss the advantages and most common misconceptions around this quantity. We consider the intuition behind different formulations of R, the complexities in its estimation (including the unavoidable lags involved), and its value compared to other indicators (e.g. the growth rate) that can be directly observed from aggregate surveillance data and react more promptly to changes in epidemic trend. As models become more sophisticated, with age and/or spatial structure, formulating R becomes increasingly complicated and inevitably model-dependent. We present some models currently used in the UK pandemic response as examples. Ultimately, limitations in the available data streams, data quality and time constraints force pragmatic choices to be made on a quantity that is an average across time, space, social structure and settings. Effectively communicating these challenges is important but often difficult in an emergency.
Item Type: | Journal Article | |||||||||||||||||||||||||||||||||||||||||||||
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Subjects: | Q Science > QA Mathematics Q Science > QR Microbiology > QR355 Virology R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics |
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Library of Congress Subject Headings (LCSH): | Viruses -- Reproduction -- Mathematical models, COVID-19 (Disease) -- Epidemiology -- Mathematical models, COVID-19 (Disease) -- Forecasting -- Mathematical models, COVID-19 (Disease) -- Transmission -- Statistical methods | |||||||||||||||||||||||||||||||||||||||||||||
Journal or Publication Title: | Journal of the Royal Statistical Society: Series A (Statistics in Society) | |||||||||||||||||||||||||||||||||||||||||||||
Publisher: | John Wiley & Sons Ltd | |||||||||||||||||||||||||||||||||||||||||||||
ISSN: | 0964-1998 | |||||||||||||||||||||||||||||||||||||||||||||
Official Date: | 22 November 2022 | |||||||||||||||||||||||||||||||||||||||||||||
Dates: |
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Volume: | 185 | |||||||||||||||||||||||||||||||||||||||||||||
Number: | S1 | |||||||||||||||||||||||||||||||||||||||||||||
Page Range: | pp. 112-130 | |||||||||||||||||||||||||||||||||||||||||||||
DOI: | 10.1111/rssa.12955 | |||||||||||||||||||||||||||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||||||||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||||||||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||||||||||||||||||||||||||
Date of first compliant deposit: | 20 December 2022 | |||||||||||||||||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 20 December 2022 | |||||||||||||||||||||||||||||||||||||||||||||
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