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      Using internet search queries for infectious disease surveillance: screening diseases for suitability

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          Abstract

          Background

          Internet-based surveillance systems provide a novel approach to monitoring infectious diseases. Surveillance systems built on internet data are economically, logistically and epidemiologically appealing and have shown significant promise. The potential for these systems has increased with increased internet availability and shifts in health-related information seeking behaviour. This approach to monitoring infectious diseases has, however, only been applied to single or small groups of select diseases. This study aims to systematically investigate the potential for developing surveillance and early warning systems using internet search data, for a wide range of infectious diseases.

          Methods

          Official notifications for 64 infectious diseases in Australia were downloaded and correlated with frequencies for 164 internet search terms for the period 2009–13 using Spearman’s rank correlations. Time series cross correlations were performed to assess the potential for search terms to be used in construction of early warning systems.

          Results

          Notifications for 17 infectious diseases (26.6%) were found to be significantly correlated with a selected search term. The use of internet metrics as a means of surveillance has not previously been described for 12 (70.6%) of these diseases. The majority of diseases identified were vaccine-preventable, vector-borne or sexually transmissible; cross correlations, however, indicated that vector-borne and vaccine preventable diseases are best suited for development of early warning systems.

          Conclusions

          The findings of this study suggest that internet-based surveillance systems have broader applicability to monitoring infectious diseases than has previously been recognised. Furthermore, internet-based surveillance systems have a potential role in forecasting emerging infectious disease events, especially for vaccine-preventable and vector-borne diseases.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12879-014-0690-1) contains supplementary material, which is available to authorized users.

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          Most cited references26

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          Predicting the Present with Google Trends

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            Digital disease detection--harnessing the Web for public health surveillance.

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              Google trends: a web-based tool for real-time surveillance of disease outbreaks.

              Google Flu Trends can detect regional outbreaks of influenza 7-10 days before conventional Centers for Disease Control and Prevention surveillance systems. We describe the Google Trends tool, explain how the data are processed, present examples, and discuss its strengths and limitations. Google Trends shows great promise as a timely, robust, and sensitive surveillance system. It is best used for surveillance of epidemics and diseases with high prevalences and is currently better suited to track disease activity in developed countries, because to be most effective, it requires large populations of Web search users. Spikes in search volume are currently hard to interpret but have the benefit of increasing vigilance. Google should work with public health care practitioners to develop specialized tools, using Google Flu Trends as a blueprint, to track infectious diseases. Suitable Web search query proxies for diseases need to be established for specialized tools or syndromic surveillance. This unique and innovative technology takes us one step closer to true real-time outbreak surveillance.
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                Author and article information

                Contributors
                gabriel.milinovich@qut.edu.au
                simon.m.r.avril@gmail.com
                director.rsph@anu.edu.au
                john.brownstein@childrens.harvard.edu
                s.tong@qut.edu.au
                w2.hu@qut.edu.au
                Journal
                BMC Infect Dis
                BMC Infect. Dis
                BMC Infectious Diseases
                BioMed Central (London )
                1471-2334
                31 December 2014
                31 December 2014
                2014
                : 14
                : 1
                : 690
                Affiliations
                [ ]School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
                [ ]Infectious Disease Epidemiology Unit, School of Population Health, The University of Queensland, Brisbane, Australia
                [ ]Freelance developer, Bundaberg, Australia
                [ ]Research School of Population Health, ANU College of Medicine, Biology and Environment, The Australian National University, Canberra, Australia
                [ ]Department of Pediatrics, Harvard Medical School and Children’s Hospital Informatics Program, Boston Children’s Hospital, Boston, USA
                Article
                690
                10.1186/s12879-014-0690-1
                4300155
                25551277
                24ea38e7-66f2-40c7-b832-9e6007e96eb0
                © Milinovich et al.; licensee BioMed Central. 2014

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 5 December 2014
                : 9 December 2014
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2014

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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