Rajan Patel
Rajan Patel is a statistician in Search Quality working to develop better ways to evaluate the quality of search results and develop new signals to improve the ranking of search results. For more information, you can visit his Emory faculty profile or his webpage.
Before joining Google, Rajan was a Biostatistics Manager at Amgen, Inc. where he designed and analyzed data from pre-clinical and Phase 1 clinical trials.
Rajan received a Ph.D. in Biostatistics from Emory University in early 2006, a Masters in Computer Science from Rice University in 2002, and a Bachelors of Arts in both Economics and Computer Science from Rice in 2001.
Before joining Google, Rajan was a Biostatistics Manager at Amgen, Inc. where he designed and analyzed data from pre-clinical and Phase 1 clinical trials.
Rajan received a Ph.D. in Biostatistics from Emory University in early 2006, a Masters in Computer Science from Rice University in 2002, and a Bachelors of Arts in both Economics and Computer Science from Rice in 2001.
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We present a logs-based comparison of search patterns across three platforms: computers, iPhones and conventional mobile phones. Our goal is to understand how mobile search users differ from computer-based search users, and we focus heavily on the distribution and variability of tasks that users perform from each platform. The results suggest that search usage is much more focused for the average mobile user than for the average computer-based user. However, search behavior on high-end phones resembles computer-based search behavior more so than mobile search behavior. A wide variety of implications follow from these findings. First, there is no single search interface which is suitable for all mobile phones. We suggest that for the higher-end phones, a close integration with the standard computer-based interface (in terms of personalization and available feature set) would be beneficial for the user, since these
phones seem to be treated as an extension of the users' computer. For all other phones, there is a huge opportunity for personalizing the search experience for the user's "mobile needs", as these users are likely to repeatedly search for a single type of information need on their phone.
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Detecting influenza epidemics using search engine query data
Jeremy Ginsberg
Matthew Mohebbi
Lynnette Brammer
Mark Smolinski
Larry Brilliant
Nature, vol. 457 (2009), pp. 1012-1014
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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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