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.