Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wave.
dc.contributor.author | Birrell, Paul | |
dc.contributor.author | Blake, Joshua | |
dc.contributor.author | van Leeuwen, Edwin | |
dc.contributor.author | Gent, Nick | |
dc.contributor.author | De Angelis, Daniela | |
dc.date.accessioned | 2021-12-15T12:12:31Z | |
dc.date.available | 2021-12-15T12:12:31Z | |
dc.date.issued | 2021-07-19 | |
dc.identifier.issn | 0962-8436 | |
dc.identifier.other | rstb20200279 | |
dc.identifier.uri | https://www.repository.cam.ac.uk/handle/1810/331489 | |
dc.description | Funder: Public Health England; Id: http://dx.doi.org/10.13039/501100002141 | |
dc.description.abstract | 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'. | |
dc.language | en | |
dc.publisher | The Royal Society | |
dc.subject | ARTICLES | |
dc.subject | Research articles | |
dc.subject | real-time | |
dc.subject | dynamics | |
dc.subject | COVID-19 | |
dc.subject | Bayesian | |
dc.subject | nowcasting | |
dc.subject | forecasting | |
dc.title | Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wave. | |
dc.type | Article | |
dc.date.updated | 2021-12-15T12:12:31Z | |
prism.issueIdentifier | 1829 | |
prism.publicationName | Philos Trans R Soc Lond B Biol Sci | |
prism.volume | 376 | |
dc.identifier.doi | 10.17863/CAM.78943 | |
dcterms.dateAccepted | 2021-04-12 | |
rioxxterms.versionofrecord | 10.1098/rstb.2020.0279 | |
rioxxterms.version | AO | |
rioxxterms.version | VoR | |
rioxxterms.licenseref.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.contributor.orcid | Birrell, Paul [0000-0001-8131-4893] | |
dc.contributor.orcid | De Angelis, Daniela [0000-0001-6619-6112] | |
dc.identifier.eissn | 1471-2970 | |
pubs.funder-project-id | Engineering and Physical Sciences Research Council (EP/R018561/1) | |
cam.issuedOnline | 2021-05-31 |
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