Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model
| dc.contributor.author | Dukic, Vanja | |
| dc.contributor.author | HEDIBERT FREITAS LOPES | |
| dc.contributor.author | Polson, Nicholas G. | |
| dc.coverage.cidade | Não informado | pt_BR | 
| dc.coverage.pais | Não Informado | pt_BR | 
| dc.creator | Dukic, Vanja | |
| dc.creator | Polson, Nicholas G. | |
| dc.date.accessioned | 2022-08-19T21:17:20Z | |
| dc.date.available | 2022-08-19T21:17:20Z | |
| dc.date.issued | 2012 | |
| dc.description.other | 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. This article has online supplementary materials. | pt_BR | 
| dc.format.extent | p. 1410-1426 | pt_BR | 
| dc.format.medium | Digital | pt_BR | 
| dc.identifier.issue | 500 | pt_BR | 
| dc.identifier.uri | https://repositorio.insper.edu.br/handle/11224/4061 | |
| dc.identifier.volume | 107 | pt_BR | 
| dc.language.iso | Inglês | pt_BR | 
| dc.publisher | American Statistical Association | pt_BR | 
| dc.relation.ispartof | Journal of the American Statistical Association | pt_BR | 
| dc.rights.license | O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR. | pt_BR | 
| dc.subject.keywords | Flu | pt_BR | 
| dc.subject.keywords | Google correlate | pt_BR | 
| dc.subject.keywords | Google searches | pt_BR | 
| dc.subject.keywords | Google trends | pt_BR | 
| dc.subject.keywords | H1N1 | pt_BR | 
| dc.subject.keywords | Infectious Diseases | pt_BR | 
| dc.subject.keywords | Influenza | pt_BR | 
| dc.subject.keywords | IP surveilance | pt_BR | 
| dc.subject.keywords | Nowcasting | pt_BR | 
| dc.subject.keywords | Online surveillance | pt_BR | 
| dc.subject.keywords | Particle filtering | pt_BR | 
| dc.title | Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model | pt_BR | 
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| local.identifier.sourceUri | https://www.jstor.org/stable/23427343 | |
| local.subject.cnpq | Ciências Sociais Aplicadas | pt_BR | 
| local.type | Artigo Científico | pt_BR | 
| relation.isAuthorOfPublication | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca | |
| relation.isAuthorOfPublication.latestForDiscovery | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca | 
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