Detecting influenza epidemics using search engine query data Export

@article{citeulike:3681665, 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.}, author = {Ginsberg, Jeremy and Mohebbi, Matthew H. and Patel, Rajan S. and Brammer, Lynnette and Smolinski, Mark S. and Brilliant, Larry}, 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}, issn = {0028-0836}, journal = {Nature}, keywords = {ehealth, epidemiology, web20}, month = {November}, number = {7232}, pages = {1012--1014}, posted-at = {2009-10-08 13:28:27}, priority = {2}, publisher = {Nature Publishing Group}, title = {Detecting influenza epidemics using search engine query data}, url = {http://dx.doi.org/10.1038/nature07634}, volume = {457}, year = {2008} }

TY - JOUR ID - citeulike:3681665 L3 - citeulike-article-id:3681665 N2 - 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. IS - 7232 JF - Nature SN - 0028-0836 EP - 1014 TI - Detecting influenza epidemics using search engine query data PB - Nature Publishing Group VL - 457 SP - 1012 KW - ehealth KW - epidemiology KW - web20 AU - Ginsberg, Jeremy AU - Mohebbi, Matthew AU - Patel, Rajan AU - Brammer, Lynnette AU - Smolinski, Mark AU - Brilliant, Larry PY - 2008/11/19/ UR - http://dx.doi.org/10.1038/nature07634 ER -