About: Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season

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About: Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season 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
title	Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season
Creator	Schneider, Paul Barnett⁴, David David, Barnett Donker², Gé Hooiveld², Mariëtte Jaw Van Gool³, Christel Jaw, Spreeuwenberg Mariëtte, Gé Paget, J Paget², John Peter, Hooiveld Spreeuwenberg², Peter Van Gool, Christel
topic	covid:a68add4aa14fcf7657bb80da27c6a2364c66446a#this
source	Medline; PMC
abstract	BACKGROUND: Despite the early development of Google Flu Trends in 2009, standards for digital epidemiology methods have not been established and research from European countries is scarce. AIM: In this article, we study the use of web search queries to monitor influenza-like illness (ILI) rates in the Netherlands in real time. METHODS: In this retrospective analysis, we simulated the weekly use of a prediction model for estimating the then-current ILI incidence across the 2017/18 influenza season solely based on Google search query data. We used weekly ILI data as reported to The European Surveillance System (TESSY) each week, and we removed the then-last 4 weeks from our dataset. We then fitted a prediction model based on the then-most-recent search query data from Google Trends to fill the 4-week gap (‘Nowcasting’). Lasso regression, in combination with cross-validation, was applied to select predictors and to fit the 52 models, one for each week of the season. RESULTS: The models provided accurate predictions with a mean and maximum absolute error of 1.40 (95% confidence interval: 1.09–1.75) and 6.36 per 10,000 population. The onset, peak and end of the epidemic were predicted with an error of 1, 3 and 2 weeks, respectively. The number of search terms retained as predictors ranged from three to five, with one keyword, ‘griep’ (‘flu’), having the most weight in all models. DISCUSSION: This study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data.
has issue date	2020-05-28(xsd:dateTime)
bibo:doi	10.2807/1560-7917.es.2020.25.21.1900221
bibo:pmid	32489174
has license	cc-by
sha1sum (hex)	a68add4aa14fcf7657bb80da27c6a2364c66446a
schema:url	https://doi.org/10.2807/1560-7917.es.2020.25.21.1900221
resource representing a document's title	Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season
has PubMed Central identifier	PMC7268271
has PubMed identifier	32489174
schema:publication	Euro Surveill
resource representing a document's body	covid:a68add4aa14fcf7657bb80da27c6a2364c66446a#body_text
is http://vocab.deri.ie/void#inDataset of	https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/a68add4aa14fcf7657bb80da27c6a2364c66446a
is schema:about of	named entity 'BASED' named entity 'FIT' named entity 'ARTICLE' named entity 'digital epidemiology' named entity 'search terms' named entity 'ILI' named entity 'web search queries' named entity 'influenza season' named entity 'epidemic' named entity 'Netherlands' named entity 'Google Flu Trends' named entity 'make sound' named entity 'prediction interval' named entity 'sentinel surveillance' named entity 'individual predictors' named entity 'ILI' named entity 'search queries' named entity 'sentinel surveillance' named entity 'Netherlands' named entity 'statistical models' named entity 'statistical inference' named entity 'ILI' named entity 'ILI' named entity 'confidence intervals' named entity 'ILI' named entity 'non-parametric' named entity 'rolling forecast' named entity 'data sources' named entity 'Google search' named entity 'principal component analysis' named entity 'ILI' named entity 'Dependent and independent variables' named entity 'Poland' named entity 'open license' named entity 'ILI' named entity 'ILI' named entity 'allergic asthma' named entity 'influenza-like illness' named entity 'data sources' named entity 'social media' named entity 'epidemic' named entity 'ILI' named entity 'anxiety' named entity 'epidemic' named entity 'ILI' named entity 'flu' named entity 'cross validation' named entity 'flu' named entity 'Dutch' named entity 'disease surveillance' named entity 'Google search' named entity 'Dutch' named entity 'API' named entity 'ILI' named entity '95% CI' named entity 'Wikipedia' named entity 'ILI' named entity 'MAE' named entity 'ILI' named entity 'MAE' named entity '0.98' named entity 'Pearson correlation' named entity 'variable selection' named entity 'Germany' named entity 'Valdivia' named entity 'COVID' named entity 'ILI'

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