About: Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories

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

About: Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories 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	Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories
Creator	Willem, Lander Coletti, Pietro Faes, Christel Kuylen, Elise Libin, Pieter Abrams, Steven Herzog, Sereina Petrof, Oana Wambua, James Santermans, Eva
Source	MedRxiv
abstract	Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months.
has issue date	2020-07-01(xsd:dateTime)
bibo:doi	10.1101/2020.06.29.20142851
has license	medrxiv
sha1sum (hex)	805753250dec85852be521ac4bb3a74279b11091
schema:url	https://doi.org/10.1101/2020.06.29.20142851
resource representing a document's title	Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories
resource representing a document's body	covid:805753250dec85852be521ac4bb3a74279b11091#body_text
is schema:about of	named entity 'WAVES' named entity 'TERMS' named entity 'DEATHS' named entity 'UNDERSTAND' named entity 'SURVEY DATA' named entity 'RELATED' named entity 'stochastic model' named entity 'Bayesian MCMC' named entity 'Belgium' named entity 'COVID-19' named entity 'COVID-19' named entity 'quarantine' named entity 'epidemic' named entity '95% confidence interval' named entity 'CC-BY-NC-ND 4.0' named entity 'compartmental model' named entity 'preprint' named entity 'preprint' named entity 'preprint' named entity 'asymptomatic' named entity 'IgG' named entity 'asymptomatic' named entity 'seroprevalence' named entity 'reproduction number' named entity 'time-invariant' named entity 'population model' named entity 'similar manner' named entity 'seroprevalence' named entity 'COVID' named entity 'Belgian' named entity 'exit strategies' named entity 'symptom' named entity 'MCMC' named entity 'incubation period' named entity 'IgG antibodies' named entity 'probability' named entity 'seronegative' named entity 'Phase 2a' named entity 'telework' named entity 'force of infection' named entity 'preprint' named entity 'preprint' named entity 'WHO' named entity 'physical distancing' named entity 'viral load' named entity 'nursing homes' named entity 'mortality rates' named entity 'probability' named entity 'sneezing' named entity 'nursing home' named entity 'ELISA test' named entity 'IgG' named entity 'physical distancing' named entity 'seroprevalence' named entity 'MCMC' named entity 'medRxiv' named entity 'credible interval' named entity 'seroprevalence' named entity 'hygiene' named entity 'general population' named entity 'May 18' named entity 'probability' named entity 'compartmental model' named entity 'COVID' named entity 'preprint' named entity 'peer review' named entity 'preprint' named entity 'medRxiv' named entity 'May 4' named entity 'stochastic model' named entity 'nursing homes' named entity 'asymptomatic' named entity 'medRxiv' named entity 'CC-BY-NC-ND 4.0' named entity 'logistic curve' named entity 'asymptomatic'

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