About: The effect of non-pharmaceutical interventions on COVID-19 cases, deaths and demand for hospital services in the UK: a modelling study

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About: The effect of non-pharmaceutical interventions on COVID-19 cases, deaths and demand for hospital services in the UK: a modelling study Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	The effect of non-pharmaceutical interventions on COVID-19 cases, deaths and demand for hospital services in the UK: a modelling study
Creator	Jombart, Thibaut Liu, Yang Endo, Akira Funk, Sebastian Klepac, Petra Rosello, Alicia Abbott, Sam Davies, Nicholas Diamond, Charlie Flasche, Stefan Gibbs, Hamish Gimma, Amy Hellewell, Joel Jit, Mark Sun, Fiona Prem, Kiesha Clifford, Samuel Bosse, Nikos Eggo, Rosalind Jarvis, Christopher Medley, Graham Munday, James O'reilly, Kathleen Pearson, Carl Quilty, Billy Russell, Timothy Van Zandvoort, Kevin Auzenbergs, Megan Deol, Arminder Emery, Jon Houben, Rein Knight, Gwen Nightingale, Emily Procter, Simon
source	MedRxiv
abstract	Background Non-pharmaceutical interventions have been implemented to reduce transmission of SARS-CoV-2 in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been critical to support evidence-based policymaking during the early stages of the epidemic. Methods We used a stochastic age-structured transmission model to explore a range of intervention scenarios, including the introduction of school closures, social distancing, shielding of elderly groups, self-isolation of symptomatic cases, and extreme %22lockdown%22-type restrictions. We simulated different durations of interventions and triggers for introduction, as well as combinations of interventions. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (intensive care unit, ICU) treatment, and deaths. Findings We found that mitigation measures aimed at reducing transmission would likely have decreased the reproduction number, but not sufficiently to prevent ICU demand from exceeding NHS availability. To keep ICU bed demand below capacity in the model, more extreme restrictions were necessary. In a scenario where %22lockdown%22-type interventions were put in place to reduce transmission, these interventions would need to be in place for a large proportion of the coming year in order to prevent healthcare demand exceeding availability. Interpretation The characteristics of SARS-CoV-2 mean that extreme measures are likely required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs.
has issue date	2020-04-06(xsd:dateTime)
bibo:doi	10.1101/2020.04.01.20049908
has license	medrxiv
sha1sum (hex)	7a5a8e3436a3ddc960c07cc769fc951551fda68b
schema:url	https://doi.org/10.1101/2020.04.01.20049908
resource representing a document's title	The effect of non-pharmaceutical interventions on COVID-19 cases, deaths and demand for hospital services in the UK: a modelling study
resource representing a document's body	covid:7a5a8e3436a3ddc960c07cc769fc951551fda68b#body_text
is schema:about of	named entity 'CASES' named entity 'PHARMACEUTICAL' named entity 'COVID-19' named entity 'hospital' named entity 'HOSPITAL' named entity 'DIFFERENT' covid:arg/7a5a8e3436a3ddc960c07cc769fc951551fda68b named entity 'TRANSMISSION' named entity 'TO REDUCE' named entity 'DEMAND' named entity 'EFFECT' named entity 'INTERVENTIONS' named entity 'HAVE' named entity 'STUDY' named entity 'SERVICES' named entity 'COVID-19' named entity 'MODELLING' named entity 'NON-' named entity 'DEATHS' named entity 'BASED' named entity 'CRITICAL' named entity 'IMPLEMENTED' named entity 'SIZE' named entity 'EPIDEMIC' named entity 'CONTROL' named entity 'MEASURES' named entity 'TO SUPPORT' named entity 'EARLY STAGES' named entity 'NON-' named entity 'PHARMACEUTICAL' named entity 'SARS-COV-2' named entity 'EVIDENCE' named entity 'INTERVENTIONS' named entity 'EFFECT' named entity 'POTENTIAL' named entity 'epidemic' named entity 'reduce' named entity 'epidemic' named entity 'epidemic' named entity 'pharmaceutical' named entity 'subclinical' named entity 'unitary authorities in Wales' named entity 'social distancing' named entity 'medRxiv' named entity 'clinical cases' named entity 'self-isolation' named entity 'medRxiv' named entity 'normal distribution' named entity 'clinical cases' named entity 'China' named entity 'SARS' named entity 'prediction interval'

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