Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model
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 t...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Brasil |
| Recursos: | Instituição de Ensino Superior e de Pesquisa (INSPER) |
| Repositorio: | Repositório Institucional da INSPER |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.insper.edu.br:11224/4061 |
| Acesso em linha: | https://repositorio.insper.edu.br/handle/11224/4061 |
| Access Level: | acceso abierto |
| Palavra-chave: | Flu Google correlate Google searches Google trends H1N1 Infectious Diseases Influenza IP surveilance Nowcasting Online surveillance Particle filtering |
| Resumo: | 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. |
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