About: Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015

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About: Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015 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 influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015
Creator	Wu, Wei Guan, Peng Huang, Desheng Liang, Feng
topic	covid:539ebc16737a7d43b43c582f3b625050c2bff9a8#this
Source	PMC
abstract	BACKGROUND: Influenza epidemics pose significant social and economic challenges in China. Internet search query data have been identified as a valuable source for the detection of emerging influenza epidemics. However, the selection of the search queries and the adoption of prediction methods are crucial challenges when it comes to improving predictions. The purpose of this study was to explore the application of the Support Vector Machine (SVM) regression model in merging search engine query data and traditional influenza data. METHODS: The official monthly reported number of influenza cases in Liaoning province in China was acquired from the China National Scientific Data Center for Public Health from January 2011 to December 2015. Based on Baidu Index, a publicly available search engine database, search queries potentially related to influenza over the corresponding period were identified. An SVM regression model was built to be used for predictions, and the choice of three parameters (C, γ, ε) in the SVM regression model was determined by leave-one-out cross-validation (LOOCV) during the model construction process. The model’s performance was evaluated by the evaluation metrics including Root Mean Square Error, Root Mean Square Percentage Error and Mean Absolute Percentage Error. RESULTS: In total, 17 search queries related to influenza were generated through the initial query selection approach and were adopted to construct the SVM regression model, including nine queries in the same month, three queries at a lag of one month, one query at a lag of two months and four queries at a lag of three months. The SVM model performed well when with the parameters (C = 2, γ = 0.005, ɛ = 0.0001), based on the ensemble data integrating the influenza surveillance data and Baidu search query data. CONCLUSIONS: The results demonstrated the feasibility of using internet search engine query data as the complementary data source for influenza surveillance and the efficiency of SVM regression model in tracking the influenza epidemics in Liaoning.
has issue date	2018-06-25(xsd:dateTime)
bibo:doi	10.7717/peerj.5134
bibo:pmid	29967755
has license	cc-by
sha1sum (hex)	539ebc16737a7d43b43c582f3b625050c2bff9a8
schema:url	https://doi.org/10.7717/peerj.5134
resource representing a document's title	Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015
has PubMed Central identifier	PMC6022725
has PubMed identifier	29967755
schema:publication	PeerJ
resource representing a document's body	covid:539ebc16737a7d43b43c582f3b625050c2bff9a8#body_text
is http://vocab.deri.ie/void#inDataset of	https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/539ebc16737a7d43b43c582f3b625050c2bff9a8
is schema:about of	named entity 'China' named entity 'search engine' named entity 'Public Health' named entity 'data' named entity 'acquired' named entity 'search query' named entity 'search queries' named entity 'lag' named entity 'Liaoning' named entity 'search queries' named entity 'INTEGRATING' named entity 'INTERNET' named entity 'EPIDEMICS' named entity '2011' named entity 'RESULTS' named entity 'TRADITIONAL' named entity 'CONSTRUCT' named entity 'CHALLENGES' named entity 'MONTHLY' named entity 'IMPROVING' named entity 'ACQUIRED' named entity 'USING' named entity 'DATA' named entity 'POSE' named entity 'SEARCH' named entity 'PERCENTAGE' named entity 'SOURCE' named entity 'CHOICE' named entity 'ADOPTED' named entity 'JANUARY' named entity 'INFLUENZA SURVEILLANCE' named entity 'DETERMINED BY' named entity 'THREE MONTHS' named entity 'SEARCH ENGINE' named entity 'DECEMBER' named entity 'APPROACH' named entity 'SOCIAL' named entity 'ONE MONTH' named entity 'SUPPORT VECTOR MACHINE' named entity 'EVALUATED' named entity 'TRACKING' named entity 'PUBLIC HEALTH' named entity 'SCIENTIFIC DATA' named entity 'ENSEMBLE' named entity 'INCLUDING' named entity 'INDEX' named entity 'CROSS-VALIDATION' named entity 'CENTER' named entity 'QUERY' named entity 'STUDY' named entity 'BASED' named entity 'MERGING' named entity 'PURPOSE' named entity 'TRADITIONAL' named entity 'SEARCH' named entity 'EPIDEMICS' named entity 'DATABASE' named entity 'LEAVE-ONE-OUT' named entity 'MODEL' named entity 'CHINA' named entity 'TOTAL' named entity 'ROOT MEAN SQUARE' named entity 'MEAN' named entity 'METHODS' named entity 'PERFORMED' named entity 'CASES' named entity 'SUPPORT VECTOR MACHINE' named entity 'NATIONAL' named entity '2015' named entity 'ABSOLUTE PERCENTAGE' named entity 'NUMBER OF' named entity 'REPORTED' named entity 'PARAMETERS' named entity 'SURVEILLANCE' named entity 'FORECASTING' named entity '2015'

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