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Evolution of a Search: The Use of Dynamic Twitter Searches During Superstorm Sandy
Conclusion The most important lesson learned from Superstorm Sandy was the need for a dynamic and flexible monitoring process and strategy to understand and respond quickly to health needs in the areas impacted by Superstorm Sandy. Search strategies should change as frequently as the unfolding event. The inability to adapt to a changing situation ensures stale and stagnant terminology and search results. Twitter lists and Boolean searches should be used together to maximize situational awareness. The most important information comes from the impacted population, whether news, local government or local citizens. These are t...
Source: PLOS Currents Disasters - September 26, 2014 Category: Global & Universal Authors: Sara Harris Smith Source Type: research

Google’s Flu Project Shows the Failings of Big Data
MoreGoogle Will Start Encrypting Your SearchesScientists Can Now Predict the FluGoogle Will Pay You $15 for Each Business User You Get Hooked on GoogleBig data: as buzzwords go, it’s inescapable. Gigantic corporations like SAS and IBM tout their big data analytics, while experts promise that big data—our exponentially growing ability to collect and analyze information about anything at all—will transform everything from business to sports to cooking. Big data was—no surprise—one of the major themes coming out of this month’s SXSW Interactive conference. It’s inescapable. MoreTwitter Data Reveals W...
Source: TIME.com: Top Science and Health Stories - March 13, 2014 Category: Consumer Health News Authors: Bryan Walsh Tags: Uncategorized analysis big data CDC computer flu Google google flu trends illness influenza Model prediction Search SXSW Source Type: news

CDC Reports Flu Widespread, Google Search Trends Alarming
According to new surveillance statistics released on Friday from the Centers for Disease Control and Prevention (CDC), forty-seven states in the U.S. are now reporting widespread influenza activity [1]. The virus, which first appeared in the Southeast, has reached epidemic levels. Forty-seven states have reported widespread geographic influenza activity (i.e. incidence of flu) for the week between December 30, 2012 and January 5, 2013, and twenty-four states and New York City have reported high influenza-like illness (ILI) activity (i.e. the proportion of outpatient visits to healthcare providers for influenza-like illne...
Source: Highlight HEALTH - January 15, 2013 Category: Medical Scientists Authors: Walter Jessen, Ph.D. Source Type: blogs

Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea
Conclusions: These results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data.
Source: Journal of Medical Internet Research - July 3, 2016 Category: Journals (General) Authors: Hyekyung WooYoungtae ChoEunyoung ShimJong-Koo LeeChang-Gun LeeSeong Hwan Kim Source Type: research

Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season
This study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data.
Source: Eurosurveillance - May 28, 2020 Category: Infectious Diseases Authors: Paul P Schneider, Christel JAW van Gool, Peter Spreeuwenberg, Mari ëtte Hooiveld, Gé A Donker, David J Barnett and John Paget Source Type: research

Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation
Conclusions: The use of search queries and social media for disease surveillance are relatively recent phenomena (first reported in 2006). Both the tools themselves and the methodologies for exploiting them are evolving over time. While their accuracy, speed, and cost compare favorably with existing surveillance systems, the primary challenge is to refine the data signal by reducing surrounding noise. Further developments in digital disease surveillance have the potential to improve sensitivity and specificity, passively through advances in machine learning and actively through engagement of users. Adoption, even as suppor...
Source: Journal of Medical Internet Research - July 18, 2013 Category: Journals (General) Authors: Theresa Marie BernardoAndrijana RajicIan YoungKatie RobiadekMai T PhamJulie A Funk Source Type: research

Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks.
Conclusions: This prediction method could be the core engine for implementing a (near-) real-time digital surveillance system. A digital surveillance system that uses Internet resources has enormous potential to monitor disease outbreaks in the early phase. PMID: 29181246 [PubMed]
Source: Healthcare Informatics Research - November 29, 2017 Category: Information Technology Tags: Healthc Inform Res Source Type: research

Cumulative Query Method for Influenza Surveillance Using Search Engine Data
Conclusions: Cumulative query method showed relatively higher correlation with national influenza surveillance data than combined queries in the development and validation set.
Source: Journal of Medical Internet Research - December 16, 2014 Category: Journals (General) Authors: Dong-Woo SeoMin-Woo JoChang Hwan SohnSoo-Yong ShinJaeHo LeeMaengsoo YuWon Young KimKyoung Soo LimSang-il Lee Source Type: research

Increased Internet Search Interest for GI Symptoms May Predict COVID-19 Cases in U.S. Hotspots å
Google Trends is an online tool that allows measurement of search term popularity on Google, spatially and temporally. While not an epidemiological tool for determining incidence, it can estimate the popularity of a certain disease by search volume over time.1,2 It has previously correlated well with infectious disease incidence and has demonstrated utility in disease forecasting, especially with influenza data.3 We utilized Google Trends to investigate whether search interest in common gastrointestinal (GI) symptoms would correlate with coronavirus disease 2019 (COVID-19) incidence data.
Source: Clinical Gastroenterology and Hepatology - July 2, 2020 Category: Gastroenterology Authors: Imama Ahmad, Ryan Flanagan, Kyle Staller Tags: Research Correspondence Source Type: research

Increased Internet Search Interest for GI Symptoms May Predict COVID-19 Cases in U.S. Hotspots
Google Trends is an online tool that allows measurement of search term popularity on Google, spatially and temporally. While not an epidemiological tool for determining incidence, it can estimate the popularity of a certain disease by search volume over time.1,2 It has previously correlated well with infectious disease incidence and has demonstrated utility in disease forecasting, especially with influenza data.3 We utilized Google Trends to investigate whether search interest in common gastrointestinal (GI) symptoms would correlate with coronavirus disease 2019 (COVID-19) incidence data.
Source: Clinical Gastroenterology and Hepatology - July 2, 2020 Category: Gastroenterology Authors: Imama Ahmad, Ryan Flanagan, Kyle Staller Tags: Research Correspondence Source Type: research

Real-time estimation of disease activity in emerging outbreaks using internet search information
by Emily L. Aiken, Sarah F. McGough, Maimuna S. Majumder, Gal Wachtel, Andre T. Nguyen, Cecile Viboud, Mauricio Santillana Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due t o data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Inter...
Source: PLoS Computational Biology - August 16, 2020 Category: Biology Authors: Emily L. Aiken Source Type: research

A spatial model of the efficiency of T cell search in the influenza-infected lung
Publication date: 7 June 2016 Source:Journal of Theoretical Biology, Volume 398 Author(s): Drew Levin, Stephanie Forrest, Soumya Banerjee, Candice Clay, Judy Cannon, Melanie Moses, Frederick Koster Emerging strains of influenza, such as avian H5N1 and 2009 pandemic H1N1, are more virulent than seasonal H1N1 influenza, yet the underlying mechanisms for these differences are not well understood. Subtle differences in how a given strain interacts with the immune system are likely a key factor in determining virulence. One aspect of the interaction is the ability of T cells to locate the foci of the infection in time ...
Source: Journal of Theoretical Biology - March 28, 2016 Category: Biology Source Type: research

Analysis of highway traffic indices using internet search data - Ryu I, Lee J, Park GC, Choi K, Hwang JM.
Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that...
Source: SafetyLit - May 10, 2016 Category: Global & Universal Tags: Media, Marketing, and Internet Issues Source Type: news