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 year. 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 fatalities. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza. 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.
journal_name
Naturejournal_title
Natureauthors
Ginsberg J,Mohebbi MH,Patel RS,Brammer L,Smolinski MS,Brilliant Ldoi
10.1038/nature07634subject
Has Abstractpub_date
2009-02-19 00:00:00pages
1012-4issue
7232eissn
0028-0836issn
1476-4687pii
nature07634journal_volume
457pub_type
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