Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
Publication Date
October 29, 2015
Journal
PLOS Computational Biology
Authors
Mauricio Santillana, André T. Nguyen, Mark Dredze, Michael J. Paul, et al
Volume
11
Issue
10
Pages
e1004513
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004513
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004513
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26513245
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626021
Europe PMC
http://europepmc.org/abstract/MED/26513245
Web of Science
000364399700049
Scopus
84946026274
Mendeley
http://www.mendeley.com/research/combining-search-social-media-traditional-data-sources-improve-influenza-surveillance
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Mendeley | Further Information

{"title"=>"Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance", "type"=>"journal", "authors"=>[{"first_name"=>"Mauricio", "last_name"=>"Santillana", "scopus_author_id"=>"24759290000"}, {"first_name"=>"André T.", "last_name"=>"Nguyen", "scopus_author_id"=>"56835058900"}, {"first_name"=>"Mark", "last_name"=>"Dredze", "scopus_author_id"=>"14041686400"}, {"first_name"=>"Michael J.", "last_name"=>"Paul", "scopus_author_id"=>"7402403521"}, {"first_name"=>"Elaine O.", "last_name"=>"Nsoesie", "scopus_author_id"=>"54179760200"}, {"first_name"=>"John S.", "last_name"=>"Brownstein", "scopus_author_id"=>"8872411400"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84946026274", "doi"=>"10.1371/journal.pcbi.1004513", "sgr"=>"84946026274", "arxiv"=>"1508.06941", "isbn"=>"0039067259656", "pmid"=>"26513245", "issn"=>"15537358", "pui"=>"606741240"}, "id"=>"1b1bbe40-1d9b-3165-9906-175f87549609", "abstract"=>"We present a machine learning-based methodology capable of providing real-time (\"nowcast\") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013-2014 (retrospective) and 2014-2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method's predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT's real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons.", "link"=>"http://www.mendeley.com/research/combining-search-social-media-traditional-data-sources-improve-influenza-surveillance", "reader_count"=>128, "reader_count_by_academic_status"=>{"Unspecified"=>6, "Professor > Associate Professor"=>3, "Librarian"=>1, "Researcher"=>23, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>39, "Student > Postgraduate"=>6, "Student > Master"=>23, "Other"=>8, "Student > Bachelor"=>8, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>6, "Professor > Associate Professor"=>3, "Librarian"=>1, "Researcher"=>23, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>39, "Student > Postgraduate"=>6, "Student > Master"=>23, "Other"=>8, "Student > Bachelor"=>8, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>12, "Agricultural and Biological Sciences"=>15, "Business, Management and Accounting"=>1, "Chemistry"=>2, "Computer Science"=>29, "Decision Sciences"=>3, "Earth and Planetary Sciences"=>1, "Energy"=>1, "Engineering"=>8, "Environmental Science"=>4, "Biochemistry, Genetics and Molecular Biology"=>1, "Nursing and Health Professions"=>2, "Mathematics"=>11, "Medicine and Dentistry"=>23, "Physics and Astronomy"=>2, "Psychology"=>3, "Social Sciences"=>9, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>23}, "Social Sciences"=>{"Social Sciences"=>9}, "Decision Sciences"=>{"Decision Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>3}, "Mathematics"=>{"Mathematics"=>11}, "Unspecified"=>{"Unspecified"=>12}, "Environmental Science"=>{"Environmental Science"=>4}, "Engineering"=>{"Engineering"=>8}, "Chemistry"=>{"Chemistry"=>2}, "Energy"=>{"Energy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>15}, "Computer Science"=>{"Computer Science"=>29}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>1, "United States"=>4, "Japan"=>1, "Brazil"=>1, "Poland"=>1, "Denmark"=>1, "Mexico"=>1, "Spain"=>2}, "group_count"=>10}

CrossRef

Scopus | Further Information

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Figshare

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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