About: Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases
Creator	André, Luiz Barroso Da Silva, Eveliny Cristina, Daniele Fernandes Fontes, Jesus Granzotto, Tita Martins Morita, Lia Soares De Oliveira, Anderson Zardo, Ribeiro
source	MedRxiv
abstract	The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases, beyond constant fear of the collapse in their health systems. Since the beginning of the pandemic, researchers and authorities are mainly concerned with carrying out quantitative studies (modeling and predictions) overcoming the scarcity of tests that lead us to under- reporting cases. To address these issues, we introduce a Bayesian approach to the SIR model with correction for under-reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period, along with the more likely date when the pandemic peak may occur. Several under-reporting scenarios were considered in the simulation study, showing how impacting is the lack of information in the modeling.
has issue date	2020-05-25(xsd:dateTime)
bibo:doi	10.1101/2020.05.24.20112029
has license	medrxiv
sha1sum (hex)	3e06b6edf733fdbcec4ef4b7818544bdbb27d410
schema:url	https://doi.org/10.1101/2020.05.24.20112029
resource representing a document's title	Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases
resource representing a document's body	covid:3e06b6edf733fdbcec4ef4b7818544bdbb27d410#body_text
is schema:about of	named entity 'HOW' named entity 'PROPOSED' named entity 'NUMBER OF' named entity 'DISEASE' named entity 'STARTED' named entity 'BRAZIL' named entity 'BEGINNING' named entity 'PANDEMIC' named entity 'GOVERNMENTS' named entity 'COVID-19' named entity 'ESTIMATES' named entity 'HIGH' named entity 'INFECTION' named entity 'IMPACTING' named entity 'under-reporting' named entity 'lack of information' named entity 'Brazil' named entity 'COVID-19' named entity 'reported cases' named entity 'medRxiv' named entity 'medRxiv' named entity 'China' named entity 'COVID' named entity 'COVID' named entity 'infection' named entity 'CC-BY-NC-ND 4.0' named entity 'SARS' named entity 'epidemic' named entity 'disease transmission' named entity 'Runge-Kutta' named entity 'China' named entity 'CC-BY-NC-ND 4.0' named entity 'infection' named entity 'Europe' named entity 'Public Health Emergencies' named entity 'infectious disease' named entity 'epidemic disease' named entity 'COVID-19' named entity 'fatality rate' named entity 'pathogen' named entity 'medRxiv' named entity '0.1' named entity 'DIC' named entity 'Brazilian Ministry of Health' named entity 'epidemic' named entity 'COVID' named entity 'COVID-19' named entity 'SIR model' named entity 'infectious disease' named entity 'CC-BY-NC-ND 4.0' named entity 'medRxiv' named entity 'Brazil' named entity 'Pernambuco' named entity 'Paraíba' named entity 'virus' named entity 'COVID' named entity 'World Health Organization' named entity 'Brazil' named entity 'CFR' named entity 'spreading disease' named entity 'infection' named entity 'COVID' named entity 'credible intervals' named entity 'brazil' named entity 'COVID' named entity 'historical data' named entity 'Epidemic' named entity 'index case' named entity 'mortality rate' named entity 'Asian' named entity 'vector' named entity 'lognormal distribution' named entity 'Chikungunya outbreaks' named entity 'epidemics' named entity 'Metropolis-Hastings algorithm' named entity 'virus' named entity 'SIR model' named entity 'COVID'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software