Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3, 4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.
%0 Journal Article
%1 Ginsberg2009Detecting
%A Ginsberg, Jeremy
%A Mohebbi, Matthew H.
%A Patel, Rajan S.
%A Brammer, Lynnette
%A Smolinski, Mark S.
%A Brilliant, Larry
%D 2009
%I Nature Publishing Group
%J Nature
%K networks epidemics internet
%N 7232
%P 1012--1014
%R 10.1038/nature07634
%T Detecting influenza epidemics using search engine query data
%U http://dx.doi.org/10.1038/nature07634
%V 457
%X Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3, 4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.
@article{Ginsberg2009Detecting,
abstract = {{Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3, 4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.}},
added-at = {2019-06-10T14:53:09.000+0200},
author = {Ginsberg, Jeremy and Mohebbi, Matthew H. and Patel, Rajan S. and Brammer, Lynnette and Smolinski, Mark S. and Brilliant, Larry},
biburl = {https://www.bibsonomy.org/bibtex/2a8a02cb7b3b4956966d1f408c9d41ad3/nonancourt},
citeulike-article-id = {3681665},
citeulike-linkout-0 = {http://dx.doi.org/10.1038/nature07634},
citeulike-linkout-1 = {http://dx.doi.org/10.1038/nature07634},
citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19020500},
citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19020500},
day = 19,
doi = {10.1038/nature07634},
interhash = {759445ddd405772bbbf1cc94c6b1a51d},
intrahash = {a8a02cb7b3b4956966d1f408c9d41ad3},
issn = {0028-0836},
journal = {Nature},
keywords = {networks epidemics internet},
month = feb,
number = 7232,
pages = {1012--1014},
pmid = {19020500},
posted-at = {2009-03-05 14:26:35},
priority = {2},
publisher = {Nature Publishing Group},
timestamp = {2019-08-01T16:08:54.000+0200},
title = {{Detecting influenza epidemics using search engine query data}},
url = {http://dx.doi.org/10.1038/nature07634},
volume = 457,
year = 2009
}