How Google Predicts Flu Activity

Online Master of Public Health program, MPH@Simmons
George Washington University
Milken Institute School of Public Health
Online Master of Public Health (MPH) program. GRE waivers available.
Gillings School of Global Public Health
Online Master of Public Health program, MPH@UNC
Benedictine University
CEPH Accredited Online MPH Program
Capella University
Online Master's in Public Health (MPH) and Doctor of Public Health (DrPH)

How many of you knew Google used their own keyword data to predict the onset of flu activity? Google Flu Trends uses aggregated Google search data to estimate current flu activity around the world in near real-time.

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.

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