About: Inferring the number of COVID-19 cases from recently reported deaths

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About: Inferring the number of COVID-19 cases from recently reported deaths 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	Inferring the number of COVID-19 cases from recently reported deaths
Creator	Jombart, Thibaut Liu, Yang Funk, Sebastian Edmunds, John Kucharski, Adam Abbott, Sam Clifford, Sam Gibbs, Hamish Gimma, Amy Bosse, Nikos Eggo, Rosalind Jarvis, Christopher Pearson, Carl Russell, Timothy Van Zandvoort, Kevin
source	MedRxiv; Medline
abstract	We estimate the number of COVID-19 cases from newly reported deaths in a population without previous reports. Our results suggest that by the time a single death occurs, hundreds to thousands of cases are likely to be present in that population. This suggests containment via contact tracing will be challenging at this point, and other response strategies should be considered. Our approach is implemented in a publicly available, user-friendly, online tool.
has issue date	2020-03-13(xsd:dateTime)
bibo:doi	10.1101/2020.03.10.20033761
bibo:pmid	32511459
has license	medrxiv
sha1sum (hex)	26e362e5201d11bd355a7fbb5b2b87590b1a529e
schema:url	https://doi.org/10.1101/2020.03.10.20033761
resource representing a document's title	Inferring the number of COVID-19 cases from recently reported deaths
has PubMed identifier	32511459
schema:publication	medRxiv : the preprint server for health sciences
resource representing a document's body	covid:26e362e5201d11bd355a7fbb5b2b87590b1a529e#body_text
is schema:about of	named entity 'hundreds' named entity 'number' named entity 'COVID-19' named entity 'LIKELY' named entity 'COVID-19' named entity 'TIME' named entity 'cases' named entity 'population' named entity 'population' named entity 'COVID-19' named entity 'medRxiv' named entity 'transmissibility' named entity 'peer review' named entity 'epidemiological' named entity 'serial interval' named entity 'R scripts' named entity 'epidemic' named entity 'epidemic' named entity 'epidemic' named entity 'epidemic' named entity 'epidemic' named entity 'expected number' named entity 'March 13, 2020' named entity 'Poisson' named entity 'probability mass function' named entity 'medRxiv' named entity 'social distancing' named entity 'Geometric distribution' named entity 'March 13, 2020' named entity 'medRxiv' named entity 'medRxiv' named entity 'epidemic' named entity 'medRxiv' named entity 'COVID-19' named entity 'March 13, 2020' named entity 'disease transmission' named entity 'serial interval' named entity 'March 13, 2020' named entity 'peer review' named entity 'COVID-19' named entity 'March 13, 2020' named entity 'COVID-19' named entity 'stochastic' named entity 'standard deviation' named entity 'Negative Binomial distribution' named entity 'epidemic' named entity 'peer review' named entity 'Italy' named entity 'Spain' named entity '4.7' named entity 'epidemic' named entity 'France' named entity 'peer review' named entity 'standard deviation' named entity 'simulation model' named entity 'March 13, 2020' named entity 'COVID-19' named entity 'epidemic' named entity 'epidemic' named entity 'Gamma distribution' named entity 'medRxiv' named entity 'peer review' named entity 'contact tracing' named entity 'CC-BY 4.0 International license' named entity 'R software' named entity 'Contact tracing' named entity 'Lognormal distribution' named entity 'epidemic'

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