TY - JOUR
T1 - Combining models to generate a consensus effective reproduction number R for the COVID-19 epidemic status in England
AU - Nowcasts Model Contributing Group
AU - Manley, Harrison
AU - Park, Josie
AU - Bevan, Luke
AU - Sanchez-Marroquin, Alberto
AU - Danelian, Gabriel
AU - Bayley, Thomas
AU - Bowman, Veronica
AU - Maishman, Thomas
AU - Finnie, Thomas
AU - Charlett, André
AU - Watkins, Nicholas A.
AU - Hutchinson, Johanna
AU - Medley, Graham
AU - Riley, Steven
AU - Panovska-Griffiths, Jasmina
AU - Birrell, Paul J.
AU - De Angelis, Daniela
AU - Keeling, Matt
AU - Pellis, Lorenzo
AU - Baguelin, Marc
AU - Ackland, Graeme J.
AU - Read, Jonathan
AU - Jewell, Christopher
AU - Challen, Robert
N1 - Publisher Copyright:
© The Author(s), 2024.
PY - 2024/3/14
Y1 - 2024/3/14
N2 - The effective reproduction number R was widely accepted as a key indicator during the early stages of the COVID-19 pandemic. In the UK, the R value published on the UK Government Dashboard has been generated as a combined value from an ensemble of epidemiological models via a collaborative initiative between academia and government. In this paper, we outline this collaborative modelling approach and illustrate how, by using an established combination method, a combined R estimate can be generated from an ensemble of epidemiological models. We analyse the R values calculated for the period between April 2021 and December 2021, to show that this R is robust to different model weighting methods and ensemble sizes and that using heterogeneous data sources for validation increases its robustness and reduces the biases and limitations associated with a single source of data. We discuss how R can be generated from different data sources and show that it is a good summary indicator of the current dynamics in an epidemic.
AB - The effective reproduction number R was widely accepted as a key indicator during the early stages of the COVID-19 pandemic. In the UK, the R value published on the UK Government Dashboard has been generated as a combined value from an ensemble of epidemiological models via a collaborative initiative between academia and government. In this paper, we outline this collaborative modelling approach and illustrate how, by using an established combination method, a combined R estimate can be generated from an ensemble of epidemiological models. We analyse the R values calculated for the period between April 2021 and December 2021, to show that this R is robust to different model weighting methods and ensemble sizes and that using heterogeneous data sources for validation increases its robustness and reduces the biases and limitations associated with a single source of data. We discuss how R can be generated from different data sources and show that it is a good summary indicator of the current dynamics in an epidemic.
KW - COVID-19
KW - ensemble modelling
KW - reproduction number R
KW - statistical analysis
UR - http://www.scopus.com/inward/record.url?scp=85187930629&partnerID=8YFLogxK
U2 - 10.1017/S0950268824000347
DO - 10.1017/S0950268824000347
M3 - Article
AN - SCOPUS:85187930629
SN - 0950-2688
VL - 152
JO - Epidemiology and Infection
JF - Epidemiology and Infection
M1 - e59
ER -