About: Using epidemic simulators for monitoring an ongoing epidemic

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

About: Using epidemic simulators for monitoring an ongoing epidemic 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	Using epidemic simulators for monitoring an ongoing epidemic
Creator	Raghavan, Mohan Sridharan, Kousik
source	MedRxiv
abstract	Prediction of infection trends, estimating the efficacy of contact tracing, testing or impact of influx of infected are of vital importance for administration during an ongoing epidemic. Most effective methods currently are empirical in nature and their relation to parameters of interest to administrators are not evident. We thus propose a modified SEIRD model that is capable of modeling effect of interventions and in migrations on the progress of an epidemic. The tunable parameters of this model bear relevance to monitoring of an epidemic. This model was used to show that some of the commonly seen features of cumulative infections in real data can be explained by piece wise constant changes in interventions and population influx. We also show that the data of cumulative infections from twelve Indian states between mid March and mid April 2020 can be generated from the model by applying interventions according to a set of heuristic rules. Prediction for the next ten days based on this model, reproduced real data very well. In addition, our model also reproduced the time series of recoveries and deaths. Our work constitutes an important first step towards an effective dashboard for the monitoring of epidemic by the administration, especially in an Indian context.
has issue date	2020-05-12(xsd:dateTime)
bibo:doi	10.1101/2020.05.08.20095463
has license	medrxiv
sha1sum (hex)	92ea1980ea8bd1105a53e6b8cc132d2448199864
schema:url	https://doi.org/10.1101/2020.05.08.20095463
resource representing a document's title	Using epidemic simulators for monitoring an ongoing epidemic
resource representing a document's body	covid:92ea1980ea8bd1105a53e6b8cc132d2448199864#body_text
is schema:about of	named entity 'INTERVENTIONS' named entity 'IMPACT' named entity 'ADMINISTRATION' named entity 'DAYS' named entity 'CHANGES' named entity 'EPIDEMIC' named entity 'ADDITION' named entity 'METHODS' named entity 'WORK' named entity 'CAPABLE' named entity 'VERY WELL' named entity 'STATES' named entity 'MODEL' named entity 'OF INTEREST' named entity 'EXPLAINED' named entity 'PREDICTION' named entity 'CONSTANT' named entity 'epidemic' named entity 'infection' named entity 'epidemic' named entity 'epidemic' named entity 'medRxiv' named entity 'social distancing' named entity 'COVID' named entity 'contact tracing' named entity 'medRxiv' named entity 'social distancing' named entity 'community transmission' named entity 'log scale' named entity 'contact tracing' named entity 'transmission rate' named entity 'epidemic' named entity 'standard model' named entity 'epidemic' named entity 'epidemiological data' named entity 'contact tracing' named entity 'cruise ship' named entity 'medRxiv' named entity 'probability' named entity 'evolution' named entity 'epidemic' named entity 'contact tracing' named entity 'medRxiv' named entity 'epidemic' named entity 'probability' named entity 'directly related' named entity 'maximum likelihood' named entity 'epidemic' named entity 'Contact tracing' named entity 'rate of mortality' named entity 'SEIR' named entity 'medRxiv' named entity 'population groups' named entity 'data set' named entity 'confidence intervals' named entity 'medRxiv' named entity 'epidemic model' named entity 'compartmental models' named entity 'phase change' named entity 'Indian government' named entity 'Plateau effect' named entity 'medRxiv' named entity 'SARS' named entity 'infection' named entity 'epidemic' named entity 'contact tracing' named entity 'infection' named entity 'quarantining' named entity 'stochastic models' named entity 'medRxiv' named entity 'COVID-19' named entity '0.01' named entity 'contact tracing' named entity 'sigmoid function' named entity 'contact tracing' named entity 'epidemic' named entity 'contact tracing' named entity 'reproduction rate' named entity 'quarantining' named entity '1, 2' named entity 'medRxiv' named entity 'SEIR' named entity 'CC-BY-NC-ND 4.0' named entity 'epidemic'

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