About: Quantifying Pathogen Surveillance Using Temporal Genomic Data

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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
title	Quantifying Pathogen Surveillance Using Temporal Genomic Data
Creator	Chan, Joseph Rabadan, Raul
source	Medline; PMC
abstract	With the advent of deep sequencing, genomic surveillance has become a popular method for detection of infectious disease, supplementing information gathered by classic clinical or serological techniques to identify host-determinant markers and trace the origin of transmission. However, two main factors complicate genomic surveillance. First, pathogens exhibiting high genetic diversity demand higher levels of scrutiny to obtain an accurate representation of the entire population. Second, current systems of detection are nonuniform, with significant gaps in certain geographic locations and animal reservoirs. Despite past unforeseen pandemics like the 2009 swine-origin H1N1 influenza virus, there is no standardized way of evaluating surveillance. A more complete surveillance system should capture a greater proportion of pathogen diversity. Here we present a novel quantitative method of assessing the completeness of genomic surveillance that incorporates the time of sequence collection, as well as the pathogen’s evolutionary rate. We propose the q2 coefficient, which measures the proportion of sequenced isolates whose closest neighbor in the past is within a genetic distance equivalent to 2 years of evolution, roughly the median time of changing strain selection for influenza A vaccines. Easily interpretable and significantly faster than other methods, the q2 coefficient requires no full phylogenetic characterization or use of arbitrary clade definitions. Application of the q2 coefficient to influenza A virus confirmed poor sampling of swine and avian populations and identified regions with deficient surveillance. We demonstrate that the q2 coefficient can not only be applied to other pathogens, including dengue and West Nile viruses, but also used to describe surveillance dynamics, particularly the effects of different public health policies.
has issue date	2013-01-29(xsd:dateTime)
bibo:doi	10.1128/mbio.00524-12
bibo:pmid	23362319
has license	cc-by-nc-sa
sha1sum (hex)	1f7394c2575f1eb1a196c9f19cd4b57ddd05789b
schema:url	https://doi.org/10.1128/mbio.00524-12
resource representing a document's title	Quantifying Pathogen Surveillance Using Temporal Genomic Data
has PubMed Central identifier	PMC3560527
has PubMed identifier	23362319
schema:publication	mBio
resource representing a document's body	covid:1f7394c2575f1eb1a196c9f19cd4b57ddd05789b#body_text
is schema:about of	named entity 'PATHOGENICITY' named entity 'TO IDENTIFY' named entity 'SCRUTINY' named entity 'POPULATION' named entity 'OBTAIN' named entity 'INFLUENZA A' named entity 'PRESENT' named entity 'SPECIFIC' named entity 'VACCINES' named entity 'BUT' named entity 'MEASURE' named entity 'ASSESSING' named entity 'PARTICULAR' named entity 'COMPLETENESS' named entity 'DYNAMICS' named entity 'CAPTURE' named entity 'IDENTIFIED' named entity 'YEARS' named entity 'HISTORICAL' named entity 'TRACE' named entity 'INFLUENZA A VIRUS' named entity 'INFORMATION' named entity 'SELECTION' named entity 'POTENTIAL' named entity 'ISOLATES' named entity 'ANIMAL' named entity 'SECOND' named entity 'WEST' named entity 'CHANGING' named entity 'EVOLUTION' named entity 'PATHOGEN' named entity 'RESULTS' named entity 'MOLECULAR MARKERS' named entity 'HOST' named entity 'ASSETS' named entity 'LEVELS' named entity 'GAPS' named entity 'VIRUSES' named entity 'COMPLICATE' named entity 'EFFECTS' named entity 'COEFFICIENT' named entity 'INACCURATE' named entity 'EASILY' named entity 'SWINE' named entity 'RESERVOIRS' named entity 'ENTIRE' named entity 'DEFINING' named entity 'REPRESENTATION' named entity 'pathogen' named entity 'pathogen' named entity 'coefficient' named entity 'influenza A' named entity 'completeness' named entity 'time' named entity 'prevalence' named entity 'complete' named entity 'full' named entity 'H1N1' named entity 'Temporal' named entity 'GENOMIC DATA' named entity 'SIGNIFICANT' named entity 'EMERGENCE' named entity 'KEY' named entity 'INFECTIOUS DISEASE' named entity 'POOR' named entity 'AVIAN' named entity 'SURVEILLANCE SYSTEM' named entity 'PROGRAMS' named entity 'COLLECTION' named entity 'HIGH' named entity 'SURVEILLANCE' named entity 'PATHOGEN SURVEILLANCE' named entity 'PROVIDES' named entity 'APPLICATION' named entity 'CLASSIC' named entity 'SUPPLEMENTING' named entity 'CLADE' named entity 'HISTORY OF' named entity 'HEALTH AGENCIES' named entity 'INFECTIVITY'

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