Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Estimation of reproduction numbers in real time: Conceptual and statistical challenges
AU - JUNIPER Consortium
AU - Pellis, Lorenzo
AU - Birrell, Paul J.
AU - Blake, Joshua
AU - Overton, Christopher E.
AU - Scarabel, Francesca
AU - Stage, Helena B.
AU - Brooks‐Pollock, Ellen
AU - Danon, Leon
AU - Hall, Ian
AU - House, Thomas A.
AU - Keeling, Matt J.
AU - Read, Jonathan M.
AU - De Angelis, Daniela
PY - 2022/11/30
Y1 - 2022/11/30
N2 - The reproduction number R $$ R $$ 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 $$ R $$ , 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 $$ R $$ 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.
AB - The reproduction number R $$ R $$ 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 $$ R $$ , 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 $$ R $$ 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.
KW - ORIGINAL ARTICLE
KW - ORIGINAL ARTICLES
KW - growth rate
KW - real‐time estimation
KW - reproduction numbers
U2 - 10.1111/rssa.12955
DO - 10.1111/rssa.12955
M3 - Journal article
VL - 185
SP - S112-S130
JO - Journal of the Royal Statistical Society: Series A Statistics in Society
JF - Journal of the Royal Statistical Society: Series A Statistics in Society
SN - 0964-1998
IS - 51
ER -