%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 }