About: Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data

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About: Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data 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	Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data
Creator	Wang, Jin-Feng Hu, Mao-Gui Huang, Da-Cang Huang, Ji-Xia Xu, Cheng-Dong Zhang, Hong-Yan Sui, Daniel Huang, Citation Dz, Sui Hu, M-G Huang, J-F Zhang, H-Y
Source	PMC
abstract	The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.
has issue date	2016-06-06(xsd:dateTime)
bibo:doi	10.1371/journal.pcbi.1004876
bibo:pmid	27271698
has license	cc-by
sha1sum (hex)	b8a27d9cc6b79620fefffaf23778c2d799248682
schema:url	https://doi.org/10.1371/journal.pcbi.1004876
resource representing a document's title	Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data
has PubMed Central identifier	PMC4894584
has PubMed identifier	27271698
schema:publication	PLoS Comput Biol
resource representing a document's body	covid:b8a27d9cc6b79620fefffaf23778c2d799248682#body_text
is schema:about of	named entity 'cases' named entity 'bias' named entity 'Foot' named entity 'volume' named entity 'presented' covid:arg/b8a27d9cc6b79620fefffaf23778c2d799248682 named entity 'received' named entity 'Area' named entity 'Chinese' named entity 'disease' named entity 'platform' named entity 'primary' named entity 'absolute error' named entity 'prevalence' named entity 'data' named entity 'original' named entity 'identify' named entity 'search engine' named entity 'estimation' named entity 'reduce' named entity 'search engine' named entity 'search engine' named entity 'disease prevalence' named entity 'Hand, Foot and Mouth Disease' named entity 'disease surveillance' named entity 'composite index' named entity 'HFMD' named entity 'disease prevalence' named entity 'Big Data' named entity 'composite index' named entity 'China' named entity 'HFMD' named entity 'disease surveillance' named entity 'HFMD' named entity 'HFMD' named entity 'composite index' named entity 'HFMD' named entity 'data analyst' named entity 'Baidu' named entity 'MAE' named entity 'data analysts' named entity 'Japan' named entity 'PRD' named entity 'Baidu' named entity 'RMSE' named entity 'Internet' named entity 'data sets' named entity 'HFMD' named entity 'HFMD' named entity 'composite index' named entity 'HFMD' named entity 'search engine' named entity 'HFMD' named entity 'flu' named entity 'pandemic in 2009' named entity 'data mining techniques' named entity 'Cayce' named entity 'HFMD' named entity 'search engine' named entity 'China' named entity 'search engine' named entity 'social media sites' named entity 'search engine' named entity 'percent error' named entity 'Baidu' named entity 'HFMD' named entity 'influenza-like illness' named entity 'Big Data' named entity 'Internet' named entity '23.5%'

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