About: Forecasting COVID-19: Using SEIR-D quantitative modelling for healthcare demand and capacity

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

About: Forecasting COVID-19: Using SEIR-D quantitative modelling for healthcare demand and capacity 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	Forecasting COVID-19: Using SEIR-D quantitative modelling for healthcare demand and capacity
Creator	Bell, Michael Allman, Phil Beresford, Warren Campillo-Funollet, Eduard Clay, Jacqueline Dorey, Matthew Evans, Graham Gilchrist, Kate Gurprit, Pannu Madzvamuse, Anotida Van Yperen, James Walkley, Ryan Watson, Mark
Source	MedRxiv
abstract	Rapid evidence-based decision-making and public policy based on quantitative modelling and forecasting by local and regional National Health Service (NHS-UK) managers and planners in response to the deadly severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), a virus causing COVID-19, has largely been missing. In this pilot study, we present a data-driven epidemiological modelling framework that allows to integrate quantitative modelling, validation and forecasting based on current available local and regional datasets to investigate and mitigate the impact of COVID-19 on local NHS hospitals in terms of healthcare demand and capacity as well as allowing for a systematic evaluation of the predictive accuracy of the modelling framework for long-term forecasting. We present an epidemiological model tailored and designed to meet the needs of the local health authorities, formulated to be fitted naturally to datasets which incorporate regional and local demographics. The model yields quantitative information on the healthcare demand and capacity required to manage and mitigate the COVID pandemic at the regional level. Furthermore, the model is rigorously validated using partial historical datasets, which is then used to demonstrate the forecasting power of the model and also to quantify the risk associated with the decision taken by healthcare managers and planners. Model parameters are fully justified, these are derived purely based on the time series data available at the regional level, with minimal assumptions. Using these inferred parameters, the model is able to make predictions under which secondary waves and re-infection scenarios could occur. Hence, our modelling approach addresses one of the major criticisms associated with the lack of transparency and precision of current COVID-19 models. Our approach offers a robust quantitative modelling framework where the probability of the model giving a wrong or correct prediction can be quantified.
has issue date	2020-08-01(xsd:dateTime)
bibo:doi	10.1101/2020.07.29.20164566
has license	medrxiv
sha1sum (hex)	88ab556ead61951f14f79d308e7fb99c196df92e
schema:url	https://doi.org/10.1101/2020.07.29.20164566
resource representing a document's title	Forecasting COVID-19: Using SEIR-D quantitative modelling for healthcare demand and capacity
resource representing a document's body	covid:88ab556ead61951f14f79d308e7fb99c196df92e#body_text
is schema:about of	named entity 'LEVEL' named entity 'HEALTHCARE' named entity 'VIRUS' named entity 'MODELLING FRAMEWORK' named entity 'PARAMETERS' named entity 'AUTHORITIES' named entity 'POWER' named entity 'TIME SERIES DATA' named entity 'MANAGE' named entity 'DRIVEN' named entity 'SECONDARY' named entity 'DEMOGRAPHICS' named entity 'HOSPITALS' named entity 'WRONG' named entity 'EVALUATION' named entity 'PARTIAL' named entity 'MODEL PARAMETERS' named entity 'WAVES' named entity 'USED' named entity 'modelling and forecasting' named entity 'healthcare managers' named entity 'NHS' named entity 'time series' named entity 'evidence-based' named entity 'infection' named entity 'pilot study' named entity 'COVID-19' named entity 'world economy' named entity 'data collection' named entity 'COVID' named entity 'COVID' named entity 'blue arrow' named entity 'healthcare delivery' named entity 'decontamination' named entity 'incubation time' named entity 'similar manner' named entity 'death rate' named entity 'log-likelihood function' named entity 'Imperial College London' named entity 'care homes' named entity 'epidemiological models' named entity 'log-likelihood' named entity 'epidemiological' named entity 'Brighton and Hove City Council' named entity '50 million' named entity 'compartmental model' named entity 'COVID' named entity 'long-term' named entity 'probability' named entity 'lockdown' named entity 'antiviral drugs' named entity '95% confidence interval' named entity 'care home' named entity 'data acquisition' named entity 'sensitivity analysis' named entity 'death rate' named entity 'incubation period' named entity 'infectious diseases' named entity 'regression analysis' named entity 'death rate' named entity 'lockdown' named entity 'linear regression' named entity 'infection' named entity 'Isle of Wight' named entity 'epidemiological' named entity 'asymptomatic' named entity 'infection' named entity 'Brighton and Hove' named entity 'COVID' named entity 'decision-making process' named entity 'epidemiological model' named entity 'decision-making' named entity 'pharmaceutical'

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