{"corpus_id":122187460,"paper_sha":"fcdae0d4c50c5a389e990cdb4ae74b41f891c785","doi":"10.1080/01621459.2012.713876","arxiv_id":null,"pmid":37583443,"pmcid":"PMC10426794","mag_id":2035636354,"dblp_id":null,"acl_id":null,"title":"Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model","year":2012,"publication_date":"2012-08-14","venue":"Journal of the American Statistical Association","journal":{"name":"Journal of the American Statistical Association","pages":"1410 - 1426","volume":"107"},"journal_issn":null,"journal_title":null,"publication_types":["JournalArticle"],"pubmed_pub_types":["Journal Article"],"s2_fields_of_study":["Environmental Science","Medicine","Computer Science","Mathematics"],"reference_count":70,"citation_count":178,"influential_citation_count":10,"is_open_access":false,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":5,"s2_open_access_pdf_url":null,"s2_open_access_landing_url":null,"s2_open_access_license":null,"s2_open_access_status":null,"pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"In this article, we use Google Flu Trends data together with a sequential surveillance model based on state-space methodology to track the evolution of an epidemic process over time. We embed a classical mathematical epidemiology model [a susceptible-exposed-infected-recovered (SEIR) model] within the state-space framework, thereby extending the SEIR dynamics to allow changes through time. The implementation of this model is based on a particle filtering algorithm, which learns about the epidemic process sequentially through time and provides updated estimated odds of a pandemic with each new surveillance data point. We show how our approach, in combination with sequential Bayes factors, can serve as an online diagnostic tool for influenza pandemic. We take a close look at the Google Flu Trends data describing the spread of flu in the United States during 2003-2009 and in nine separate U.S. states chosen to represent a wide range of health care and emergency system strengths and weaknesses. 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