Real-time nowcasting and forecasting of COVID-19 dynamics in England: The first wave

Paul Birrell, Joshua Blake, Edwin Van Leeuwen, Robert Gent, Daniela De Angelis*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
27 Downloads (Pure)


England has been heavily affected by the SARS-CoV-2 pandemic, with severe 'lockdown' mitigation measures now gradually being lifted. The real-time pandemic monitoring presented here has contributed to the evidence informing this pandemic management throughout the first wave. Estimates on the 10 May showed lockdown had reduced transmission by 75%, the reproduction number falling from 2.6 to 0.61. This regionally varying impact was largest in London with a reduction of 81% (95% credible interval: 77-84%). Reproduction numbers have since then slowly increased, and on 19 June the probability of the epidemic growing was greater than 5% in two regions, South West and London. By this date, an estimated 8% of the population had been infected, with a higher proportion in London (17%). The infection-to-fatality ratio is 1.1% (0.9-1.4%) overall but 17% (14-22%) among the over-75s. This ongoing work continues to be key to quantifying any widespread resurgence, should accrued immunity and effective contact tracing be insufficient to preclude a second wave. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Original languageEnglish
Article number20200279
Number of pages9
JournalPhilosophical transactions of the Royal Society of London. Series B, Biological sciences
Issue number1829
Early online date31 May 2021
Publication statusPublished - 19 Jul 2021

Bibliographical note

Funding Information: This work was supported by the Medical Research Council (unit programme no. MC UU 00002/11) in partnership with Public Health England. In addition, J.B. received support by the EPSRC (EP/R01856/1). Prior to the pandemic, this project was developed under a grant from the National Institute for Health Research (HTA Project: 11/46/03). We gratefully acknowledge the access to the data from the United Kingdom Time Use Survey through the UK Data Service (

Open Access: Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.

Publishers Copyright: © 2021 The Authors.

Citation: Birrell Paul, Blake Joshua, van Leeuwen Edwin, Gent Nick and De Angelis Daniela 2021Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wavePhil. Trans. R. Soc. B3762020027920200279



  • Bayesian
  • COVID-19
  • dynamics
  • forecasting
  • nowcasting
  • real-time


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