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      Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network

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          Abstract

          Because influenza is a contagious respiratory illness that seriously threatens public health, accurate real-time prediction of influenza outbreaks may help save lives. In this paper, we use the Twitter data set and the United States Centers for Disease Control’s influenza-like illness (ILI) data set to predict a nearly real-time regional unweighted percentage ILI in the United States by use of an artificial neural network optimized by the improved artificial tree algorithm. The results show that the proposed method is an efficient approach to real-time prediction.

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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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            National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

            David Broniatowski, Michael J. Paul, Mark Dredze (2013)
            Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases “chatter” – messages that are about influenza but that do not pertain to an actual infection – masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012–2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system’s influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.
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              Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons.

              Mark Smolinski, Adam W. Crawley, Kristin Baltrusaitis … (2015)
              We summarized Flu Near You (FNY) data from the 2012-2013 and 2013-2014 influenza seasons in the United States.
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                Author and article information

                Contributors
                Hongping Hu:
                ORCID: http://orcid.org/0000-0001-9708-9141
                hhp92@163.com
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 20 March 2018
                Publication date PMC-release: 20 March 2018
                Publication date Collection: 2018
                Volume: 8
                Electronic Location Identifier: 4895
                Affiliations
                [1 ]GRID grid.440581.c, School of Science, , North University of China, ; Taiyuan, Shanxi 030051 PR China
                [2 ]ISNI 0000 0001 2151 2636, GRID grid.215654.1, School of Mathematical and Natural Sciences, , Arizona State University, ; Phoenix, Arizona USA
                Author information
                http://orcid.org/0000-0001-9708-9141
                http://orcid.org/0000-0003-1427-5964
                Article
                Publisher ID: 23075
                DOI: 10.1038/s41598-018-23075-1
                PMC ID: 5861130
                PubMed ID: 29559649
                SO-VID: d4aa1aba-0742-4639-bdcd-88fe6428fded
                Copyright © © The Author(s) 2018
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 11 January 2018
                Date accepted : 5 March 2018
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2018

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