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.
%0 Journal Article
%1 doi:10.1080/01621459.2012.713876
%A Dukic, Vanja
%A Lopes, Hedibert F.
%A Polson, Nicholas G.
%D 2012
%J Journal of the American Statistical Association
%K flu google trend
%N 500
%P 1410-1426
%R 10.1080/01621459.2012.713876
%T Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model
%V 107
%X 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.
@article{doi:10.1080/01621459.2012.713876,
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. This article has online supplementary materials.},
added-at = {2013-03-28T10:37:45.000+0100},
author = {Dukic, Vanja and Lopes, Hedibert F. and Polson, Nicholas G.},
biburl = {https://www.bibsonomy.org/bibtex/2b166eb92f4d61df716760a3395c5c413/ytyoun},
doi = {10.1080/01621459.2012.713876},
interhash = {1dee3bf735b4ec2ea3e0d0170b7d19d8},
intrahash = {b166eb92f4d61df716760a3395c5c413},
journal = {Journal of the American Statistical Association},
keywords = {flu google trend},
number = 500,
pages = {1410-1426},
timestamp = {2015-07-29T07:35:16.000+0200},
title = {Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model},
volume = 107,
year = 2012
}