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      Using Clinicians’ Search Query Data to Monitor Influenza Epidemics

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          Abstract

          Search query information from a clinician's database, UpToDate, is shown to predict influenza epidemics in the United States in a timely manner. Our results show that digital disease surveillance tools based on experts' databases may be able to provide an alternative, reliable, and stable signal for accurate predictions of influenza outbreaks.

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          Using internet searches for influenza surveillance.

          Philip Polgreen, Yiling Chen, David Pennock … (2008)
          The Internet is an important source of health information. Thus, the frequency of Internet searches may provide information regarding infectious disease activity. As an example, we examined the relationship between searches for influenza and actual influenza occurrence. Using search queries from the Yahoo! search engine ( http://search.yahoo.com ) from March 2004 through May 2008, we counted daily unique queries originating in the United States that contained influenza-related search terms. Counts were divided by the total number of searches, and the resulting daily fraction of searches was averaged over the week. We estimated linear models, using searches with 1-10-week lead times as explanatory variables to predict the percentage of cultures positive for influenza and deaths attributable to pneumonia and influenza in the United States. With use of the frequency of searches, our models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P < .001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P < .001). Search-term surveillance may provide an additional tool for disease surveillance.
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            HealthMap: Global Infectious Disease Monitoring through Automated Classification and Visualization of Internet Media Reports

            Clark C. Freifeld, Kenneth Mandl, Ben Reis … (2008)
            Objective Unstructured electronic information sources, such as news reports, are proving to be valuable inputs for public health surveillance. However, staying abreast of current disease outbreaks requires scouring a continually growing number of disparate news sources and alert services, resulting in information overload. Our objective is to address this challenge through the HealthMap.org Web application, an automated system for querying, filtering, integrating and visualizing unstructured reports on disease outbreaks. Design This report describes the design principles, software architecture and implementation of HealthMap and discusses key challenges and future plans. Measurements We describe the process by which HealthMap collects and integrates outbreak data from a variety of sources, including news media (e.g., Google News), expert-curated accounts (e.g., ProMED Mail), and validated official alerts. Through the use of text processing algorithms, the system classifies alerts by location and disease and then overlays them on an interactive geographic map. We measure the accuracy of the classification algorithms based on the level of human curation necessary to correct misclassifications, and examine geographic coverage. Results As part of the evaluation of the system, we analyzed 778 reports with HealthMap, representing 87 disease categories and 89 countries. The automated classifier performed with 84% accuracy, demonstrating significant usefulness in managing the large volume of information processed by the system. Accuracy for ProMED alerts is 91% compared to Google News reports at 81%, as ProMED messages follow a more regular structure. Conclusion HealthMap is a useful free and open resource employing text-processing algorithms to identify important disease outbreak information through a user-friendly interface.
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              When Google got flu wrong.

              Declan Butler (2013)
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                Author and article information

                Journal
                Journal ID (nlm-ta): Clin Infect Dis
                Journal ID (iso-abbrev): Clin. Infect. Dis
                Journal ID (publisher-id): cid
                Title: Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America
                Publisher: Oxford University Press
                ISSN (Print): 1058-4838
                ISSN (Electronic): 1537-6591
                Publication date (Print): 15 November 2014
                Publication date (Electronic): 12 August 2014
                Volume: 59
                Issue: 10
                Pages: 1446-1450
                Affiliations
                [1 ] School of Engineering and Applied Sciences, Harvard University , Cambridge
                [2 ] Children's Hospital Informatics Program, Boston Children's Hospital
                [3 ] Department of Pediatrics, Harvard Medical School , Boston
                [4 ] Department of Internal Medicine, Cambridge Health Alliance , Massachusetts
                [5 ] Department of Epidemiology, Biostatistics and Occupational Health, McGill University , Montreal, Quebec, Canada
                Author notes
                [* ]Correspondence: Mauricio Santillana, MS, PhD, School of Engineering and Applied Sciences, Harvard University, 29 Oxford St, Cambridge, MA 02138 ( msantill@ 123456fas.harvard.edu ).
                Article
                Publisher ID: ciu647
                DOI: 10.1093/cid/ciu647
                PMC ID: 4296132
                PubMed ID: 25115873
                SO-VID: 59736735-dbe4-4caf-877e-3a841bc77d4b
                Copyright © © The Author 2014. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@ 123456oup.com .
                License:

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                Date received : 18 June 2014
                Date accepted : 04 August 2014
                Categories
                Subject: Brief Reports

                ScienceOpen disciplines: Infectious disease & Microbiology
                Keywords: digital disease detection,internet-based disease surveillance,prediction of influenza
                Data availability:
                ScienceOpen disciplines: Infectious disease & Microbiology
                Keywords: digital disease detection, internet-based disease surveillance, prediction of influenza

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