About: Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking

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

About: Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking 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
has title	Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking
Creator	Johansson Id, Michael Lipsitch Id, Marc Mcgough Id, Sarah Menziesid, Nicolas
Source	PMC
abstract	Achieving accurate, real-time estimates of disease activity is challenged by delays in case reporting. “Nowcast” approaches attempt to estimate the complete case counts for a given reporting date, using a time series of case reports that is known to be incomplete due to reporting delays. Modeling the reporting delay distribution is a common feature of nowcast approaches. However, many nowcast approaches ignore a crucial feature of infectious disease transmission—that future cases are intrinsically linked to past reported cases—and are optimized to one or two applications, which may limit generalizability. Here, we present a Bayesian approach, NobBS (Nowcasting by Bayesian Smoothing) capable of producing smooth and accurate nowcasts in multiple disease settings. We test NobBS on dengue in Puerto Rico and influenza-like illness (ILI) in the United States to examine performance and robustness across settings exhibiting a range of common reporting delay characteristics (from stable to time-varying), and compare this approach with a published nowcasting software package while investigating the features of each approach that contribute to good or poor performance. We show that introducing a temporal relationship between cases considerably improves performance when the reporting delay distribution is time-varying, and we identify trade-offs in the role of moving windows to accurately capture changes in the delay. We present software implementing this new approach (R package “NobBS”) for widespread application and provide practical guidance on implementation.
has issue date	2020-04-06(xsd:dateTime)
bibo:doi	10.1371/journal.pcbi.1007735
bibo:pmid	32251464
has license	cc0
sha1sum (hex)	ad668b2a8041fcd63780fae4ad5ce13b2428811d
schema:url	https://doi.org/10.1371/journal.pcbi.1007735
resource representing a document's title	Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking
has PubMed Central identifier	PMC7162546
has PubMed identifier	32251464
schema:publication	PLoS Comput Biol
resource representing a document's body	covid:ad668b2a8041fcd63780fae4ad5ce13b2428811d#body_text
is schema:about of	named entity 'intrinsically' named entity 'good' named entity 'future' named entity 'common' named entity 'However' named entity 'reporting' named entity 'time series' named entity 'settings' named entity 'application' named entity 'case' named entity 'published' named entity 'Bayesian' named entity 'delays' named entity 'influenza-like illness' named entity 'United States' named entity 'trade-offs' named entity 'stable' named entity 'tracking' named entity 'case reports' named entity 'reported cases' named entity 'dengue' named entity 'autocorrelated' named entity '95%' named entity 'epidemics' named entity 'dengue' named entity 'ILI' named entity 'United States' named entity 'dengue' named entity 'probability' named entity 'Unif' named entity 'ILI' named entity 'influenza' named entity 'dengue' named entity 'United States' named entity 'infectious disease' named entity 'random walk' named entity 'dengue' named entity 'ILI' named entity 'point estimate' named entity 'dengue' named entity 'nowcasts' named entity 'time series' named entity 'nowcast' named entity 'ILI' named entity 'MAE' named entity '0.01' named entity 'binomial process' named entity 'ILI' named entity 'nowcast' named entity '0.01' named entity '95%' named entity 'dengue' named entity 'dengue' named entity 'nowcasts' named entity 'blue line' named entity 'nowcasts' named entity 'root mean square error' named entity 'time evolution' named entity '95%' named entity 'Moving windows' named entity 'prediction interval' named entity 'CRAN' named entity 'nowcast' named entity 'dengue' named entity 'evolution' named entity 'infectious disease' named entity 'moving windows' named entity 'ILI' named entity 'nowcast' named entity 'ILI' named entity 'healthcare providers' named entity 'probability' named entity 'negative binomial' named entity 'dengue' named entity 'influenza-like illness' named entity 'CDC' named entity 'RMSE' named entity 'random effects'

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