About: Inference of COVID-19 epidemiological distributions from Brazilian hospital data

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

About: Inference of COVID-19 epidemiological distributions from Brazilian hospital data 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
has title	Inference of COVID-19 epidemiological distributions from Brazilian hospital data
Creator	Bhatt, Samir Whittaker, Charles Hoeltgebaum, Henrique Mellan, Thomas Mishra, Swapnil Flaxman, Seth Gandy, Axel Hawryluk, Iwona Zhu, Harrison Schnekenberg, Ricardo
topic	covid:d103229bb12a60485c60dc0d00d09f395ba300f4#this
Source	MedRxiv
abstract	Knowing COVID-19 epidemiological distributions, such as the time from patient admission to death, is directly relevant to effective primary and secondary care planning, and moreover, the mathematical modelling of the pandemic generally. Here we determine epidemiological distributions for patients hospitalised with COVID-19 using a large dataset (range N=21,000-157,000) from the Brazilian SIVEP-Gripe (Sistema de Informacao de Vigilancia Epidemiologica da Gripe) database. We fit a set of probability distribution functions and estimate a symptom-onset-to-death mean of $15.2$ days for Brazil, which is lower than earlier estimates of 17.8 days based on early Chinese data. A joint Bayesian subnational model is used to simultaneously describe the 26 states and one federal district of Brazil, and shows significant variation in the mean of the symptom-onset-to-death time, with ranges between 11.2-17.8 days across the different states. We find strong evidence in favour of specific probability distribution function choices: for example, the gamma distribution gives the best fit for onset-to-death and the generalised log-normal for onset-to-hospital-discharge. Our results show that epidemiological distributions have considerable geographical variation, and provide the first estimates of these distributions in a low and middle-income setting. At the subnational level, variation in COVID-19 outcome timings are found to be correlated with poverty, deprivation and segregation levels, and weaker correlation is observed for mean age, wealth and urbanicity.
has issue date	2020-07-16(xsd:dateTime)
bibo:doi	10.1101/2020.07.15.20154617
has license	medrxiv
sha1sum (hex)	d103229bb12a60485c60dc0d00d09f395ba300f4
schema:url	https://doi.org/10.1101/2020.07.15.20154617
resource representing a document's title	Inference of COVID-19 epidemiological distributions from Brazilian hospital data
resource representing a document's body	covid:d103229bb12a60485c60dc0d00d09f395ba300f4#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/d103229bb12a60485c60dc0d00d09f395ba300f4
is schema:about of	named entity 'Here' named entity 'patient' named entity 'secondary care' named entity 'geographical' named entity 'Inference' named entity 'EFFECTIVE' named entity 'ONSET' named entity 'STRONG' named entity 'DIAGNOSIS' named entity 'PROVIDE' named entity 'FIT' named entity 'ESTIMATE' named entity 'MIDDLE' named entity 'DATASET' named entity 'LARGE' named entity 'DIFFERENT' named entity 'DEPRIVATION' named entity 'PANDEMIC' named entity 'VARIATION' named entity 'RELEVANT' named entity 'CHINESE' named entity 'HERE' named entity 'DEATH' named entity 'GEOGRAPHICAL VARIATION' named entity 'BEST' named entity 'AGE' named entity 'EARLY' named entity 'PATIENT ADMISSION' named entity 'OUR' named entity 'BRAZIL' named entity 'OBSERVED' named entity 'Sistema' named entity 'time' named entity 'Chinese' named entity 'days' named entity 'variation' named entity 'levels' named entity 'epidemiological' named entity 'set' named entity 'fit' named entity 'generally' named entity 'probability distribution function' named entity 'probability distribution' named entity 'correlated' named entity 'Bayesian' named entity 'large' named entity 'COVID-19' named entity 'mathematical modelling' named entity '21,000' named entity 'epidemiological' named entity 'Brazil' named entity 'probability distribution function' named entity 'Inference' named entity '95% CI' named entity 'model evidence' named entity 'federal district' named entity 'negatively correlated' named entity 'medRxiv' named entity 'preprint' named entity 'Brazil' named entity 'medRxiv' named entity 'credible intervals' named entity 'preprint' named entity 'Brazil' named entity 'July 16' named entity 'log-normal' named entity 'epidemic' named entity 'Prior probabilities' named entity 'socioeconomic' named entity 'Global surveillance' named entity 'preprint' named entity 'PCR' named entity 'log-normal' named entity 'Probability distribution'

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 © 2009-2024 OpenLink Software