Detecting influenza epidemics using search engine query data

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(1). 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(2). Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza(3,4). 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.