Detecting influenza epidemics using search engine query data

By Jeremy Ginsberg, Matthew H. Mohebbi, Rajan Patel, Lynnette Brammer, M. Smolinski, L. Brilliant. 2009

4,144 citations recorded by the source corpus.

Subfields: Epidemiology, Modeling and Simulation, Information Retrieval & Search, Medical & Health AI

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Citations in the corpus

The power of synergy in differential privacy: Combining a small curator with local randomizers (2019, background): “Data has become one of the main drivers of innovation in applications as varied as technology, medicine [33], and city planning [34, 53].”
Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “common search terms to predict seasonal flu is a good example of such digital byproduct [22].”
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “A well-known example of the perils in using data science methods in a theory-agnostic manner is Google Flu Trends, where a data-driven model was learned to estimate the number of inﬂuenza-related physician visits based on the number of inﬂuenza-related Google search queries in the United States [17]”
Forecasting Private Consumption: Survey-Based Indicators vs. Google Trends (2009, methodology): “Ginsberg et al. (2009) used large numbers of Google Trends search queries to estimate the current level of influenza activity in the US.”
Modeling infectious disease dynamics in the complex landscape of global health (2015, neutral): “incidence of influenza in the United States (72), to elucidate the spatial dynamics of measles and”
Towards detecting influenza epidemics by analyzing Twitter messages (2010, methodology): “A similar correlation was found in flurelated queries submitted to search engines [12, 6, 22, 8].”
Internet search behavior as an economic forecasting tool: The case of inflation expectations (2011, positive): “[14] demonstrate that Google search query data can predict actual influenza rates 1–2 weeks in advance of the publication of the US Center for Disease Control’s Influenza Sentinel Provider Surveillance Network.”
Can Google Data Help Predict French Youth Unemployment (2012, background): “Several papers use Google search data for influenza virus surveillance (Doornik, 2009; Ginsberg et al., 2009).”
Modeling and predicting behavioral dynamics on the web (2012, background): “Radinsky et al.[17] showed that queries reﬂect real-world news events, and Ginsberg et al. [8] used queries for predicting H1N1 inﬂuenza outbreak.”
Kernel Archetypal Analysis for Clustering Web Search Frequency Time Series (2014, background): “Results on the temporal dynamics of search engine queries include data driven models of the spread of diseases [12], accounts of the propagation of news [13], [14], or characterizations of the formation of political opinions [15].”
How (Not) to Predict Elections (2011, background): “…able to make predictions based on publicly available data would have numerous benefits in areas such as health (e.g. predictions of flu epidemics [5], [6]), business (e.g., prediction of box-office success of movies [7] and product marketability [4]), economics (e.g., predictions on stock…”
A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014, background): “Two recent studies published in Nature Climate Change 25 and Nature 26 came to opposing conclusions about whether there are noticeable changes in drought trends as a result of climate change.”