About: Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

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

About: Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models 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	Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models
Creator	Lloyd-Smith, James Gunson, Rory McMenamin, Jim Matthews, Louise Mair, Colette Nickbakhsh, Sema Reeve, Richard Reynolds, Arlene Murcia, Pablo
Source	Medline; PMC; WHO
abstract	It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness.
has issue date	2019-12-13(xsd:dateTime)
bibo:doi	10.1371/journal.pcbi.1007492
bibo:pmid	31834896
has license	cc-by
schema:url	https://doi.org/10.1371/journal.pcbi.1007492
resource representing a document's title	Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models
has PubMed Central identifier	PMC6934324
has PubMed identifier	31834896
schema:publication	PLoS Comput Biol
resource representing a document's body	covid:PMC6934324#body_text
is schema:about of	named entity 'ESTIMATION' named entity 'PATHOGEN' named entity 'USING' named entity 'DYNAMICS' named entity 'TEMPORAL' named entity 'MODELS' named entity 'virus' named entity 'lower triangular matrix' named entity 'prior distribution' named entity 'transmission rate' named entity 'Sampling' named entity 'virus' named entity 'pathogen' named entity 'relative risk' named entity 'covariance matrix' named entity 'covariance' named entity 'public health' named entity 'IBV' named entity 'standard deviations' named entity 'singular spectrum analysis' named entity 'autoregressive' named entity 'Acute respiratory infections' named entity 'Emtv' named entity 'diagnostic tests' named entity 'pathogens' named entity 'long term' named entity 'respiratory syncytial virus' named entity 'multiple regression' named entity 'environmental factor' named entity 'influenza B virus' named entity 'statistical methods' named entity 'Statistical methods' named entity 'infection' named entity 'virus' named entity 'virus' named entity 'autoregressive model' named entity 'confounding factors' named entity 'autoregressive structure' named entity 'pneumococcal' named entity 'public health' named entity 'pathogens' named entity 'autocorrelation' named entity 'gastrointestinal tract' named entity 'autoregression' named entity 'viruses' named entity 'pathogen' named entity 'virus' named entity 'Random effects' named entity 'pathogens' named entity 'diagonal matrix' named entity 'stationary time series' named entity 'virus' named entity 'time series data' named entity 'Glasgow' named entity 'autoregressive structure' named entity 'multiple comparisons' named entity 'time series' named entity 'time frame' named entity 'obligate intracellular pathogens' named entity 'multiple comparison' named entity 'identity matrix' named entity '0.9' named entity 'infection' named entity 'relative risk' named entity 'virus' named entity 'stationary time series' named entity 'virus' named entity 'IBV' named entity 'autoregressive model' named entity 'Bayesian' named entity 'Kronecker product' named entity 'non-stationarity' named entity 'covariance matrix' named entity 'infectious diseases'

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