Researchers at University of Iowa Leverage OneRiot's Realtime Index of the Social Web to Monitor and Track Anxiety about Infectious Diseases
Iowa City, Iowa (vocus) April 29, 2009 -- Researchers at the University of Iowa today announced a major study to track public perception of the swine flu outbreak and other infectious diseases, utilizing a unique, realtime index of the social web from OneRiot.
The project, Social Web Information Monitoring for Health, dubbed “SWIM for Health,” has the potential to enhance disease tracking and forecasting by harnessing the power of the social web. OneRiot’s technology organizes social information from across the web in realtime – including updates from Twitter and Facebook, recent blog posts, current popular search queries and other web usage activity – to provide a realtime index of information from the social web.
SWIM for Health is being conducted as part of the University of Iowa’s interdisciplinary Computational Epidemiology research group (CompEpi). The Iowa CompEpi group applies computational methods to help solve public health problems, including new disease surveillance methods and harnessing technology for disease prevention.
According to Iowa CompEpi faculty Philip Polgreen, Assistant Professor of Internal Medicine, “Analyzing and aggregating realtime data from the social web is a unique enhancement to traditional disease surveillance systems.”
SWIM for Health is spearheaded by researcher Alessio Signorini, PhD candidate at the University of Iowa and Director of Search Technology at OneRiot. Signorini will use the large amount of realtime data provided by OneRiot, in conjunction with official public health surveillance data, to discern patterns in disease diffusion and public perception of health scare outbreaks. Previous studies have shown the benefit of analyzing data abstracted from internet searches in forecasting seasonal influenza. SWIM for Health supplements such search data with OneRiot’s realtime information from across the social web, reflecting the web content people are sharing with friends (e,g, relevant videos, news stories and blog posts) as well as what they are talking about on social networks.
“The increase of social activity online, coupled with the availability of health-related resources, offers new ways to detect early outbreaks of diseases and measure public perception in realtime” said Signorini. “I hope that my research will one day enable us to more quickly and effectively understand how the public's sentiment affects the behavior and spread of diseases.”
Signorini has also developed an interactive map to monitor swine-flu-related “tweets” from Twitter in realtime. For example, if an unusually large number of people in Los Angeles tweet ‘swine flu’ or ‘tamiflu,’ it may indicate a potential influenza outbreak in southern California, or, more likely, it may simply indicate an elevated perception of disease risk. Such information might help public health authorities better address public concerns.
“By analyzing content from the realtime web, we hope to tease apart disease information from undue alarm,” said Dr. Alberto Maria Segre, Iowa CompEpi faculty and Professor of Computer Science.
The Computational Epidemiology Research (CompEpi) is an interdisciplinary group at the University of Iowa. The research uses computational tools to model, simulate, visualize and, in general, understand the spread of disease with the goal of informing public and hospital policy decisions with respect to disease surveillance, disease prevention measures, and outbreak containment. http://compepi.cs.uiowa.edu/
Launched in November 2008, OneRiot is a realtime search engine that finds news, stories and videos people are talking about right now across the social web. Unlike any other search engine, OneRiot ranks a web page’s relevance based on its current popularity with real people. OneRiot is a privately held company headquartered in Boulder, Colo. with offices in San Francisco. http://www.oneriot.com
Read the full story at http://www.prweb.com/releases/2009/04/prweb2372074.htm.
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