Using internet searches for influenza surveillance.

By Philip M. Polgreen, Yilin Chen, David M. Pennock, F. Nelson. Clinical Infectious Diseases, 2008

601 citations recorded by the source corpus.

Subfields: Epidemiology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/549547e76c64f001f9bffeaf6bdc1c1408186836

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Citations in the corpus

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance (2015, unknown): “Many attempts have been made to estimate the ILI activity in the US ahead of the release of CDC reports, some using a combination of statistical and mechanistic SIR models [3,4,5] and others using non-traditional Internet-based information systems such as: Google [6,7], Yahoo [8], and Baidu [9] Inte”
The power of prediction with social media (2013, methodology): “In fact, a number of researchers have been working not on forecasting future events but on so-called “nowcasting” or “predicting the present”.”
Internet search behavior as an economic forecasting tool: The case of inflation expectations (2011, unknown): “[28] examine Yahoo search queries and find a high correlation of the words “flu” and “influenza” with virologic and mortality surveillance data over several years.”
Accurate estimation of influenza epidemics using Google search data via ARGO (2015, background): “In recent years, methods that harness internet-based information have also been proposed, such as Google [1], Yahoo [2], and Baidu [3] internet searches, Twitter posts [4], Wikipedia article views [5], clinicians’ queries [6], and crowd sourced self-reporting mobile apps such as Influenzanet (Europe”
Predicting consumer behavior with Web search (2010, result): “More recently, Polgreen et al. (8) showed that search volume for handpicked influenza-related queries was correlated with subsequently reported caseloads over the period 2004 – 2008, and Hulth et al. (9) found similar results in a study of search queries submitted on a Swedish medical Web site.”
Towards detecting influenza epidemics by analyzing Twitter messages (2010, background): “A similar correlation was found in flurelated queries submitted to search engines [12, 6, 22, 8].”
Harvesting ambient geospatial information from social media feeds (2011, background): “For example, there has been a number of studies using data mining techniques to trawl through traditional media such as news articles (Brownstein et al. 2008) or Internet search engines (e.g. Polgreen et al. 2008), and blogs (Corley et al. 2010) to explore disease outbreaks.”
Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet (2009, background): “A number of other studies have replicated findings from the Eysenbach study on the relationship between Internet search behavior and influenza incidence [2,5,6].”
Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales (2013, background): “In parallel, novel approaches based on influenza-related internet search queries have been reported to yield faster detection and estimation of the intensity of influenza epidemics [10–16].”
The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic (2011, background): “For example, in the case of influenza, search engine query data from Yahoo [2] and Google [3] are known to be closely associated with seasonal influenza activity, and to a limited extent, actually provide some information about seasonal disease trends that precede official reports of disease activit”