Detecting influenza epidemics using search engine query data
Venue
Nature, vol. 457 (2009), pp. 1012-1014
Publication Year
2009
Authors
Jeremy Ginsberg, Matthew Mohebbi, Rajan Patel, Lynnette Brammer, Mark Smolinski, Larry Brilliant
BibTeX
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.
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