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Combining models to generate a consensus effective reproduction number R for the COVID-19 epidemic status in England

Park, J.; Bevan, L.; Sanchez-Marroquin, A.; Danelian, G.; Bayley, T.; Manley, H.; Bowman, V.; Maishman, T.; Finnie, T.; Charlett, A.; Nowcasts Models Contribution Group, ; Watkins, N. A.; Hutchinson, J.; Riley, S.; Panovska-Griffiths, J.

2023-03-01 epidemiology
10.1101/2023.02.27.23286501 medRxiv
Show abstract

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 size, 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 is therefore a good summary indicator of the current dynamics in an epidemic.

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