Big Data Analytics and Social Media to Help Model Ebola Spread
A team of researchers from the Florida Atlantic University (FAU) in the U.S. are currently working on a project that aims to develop a computer model on the spread of the Ebola virus using big data analytics and social media sites.
The team plans to extract large amounts of data from social media sites including Facebook, Twitter, and Google. The data will include information about infected individuals and patients.
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The research team intends to inject all this information into a decision support system that will model the spread of Ebola, creating graphs and predictive tools to help them track the virus and analyze the outcome.
Professor Borko Furht of the Department of Computer and Electrical Engineering and Computer Science at the FAU said, "Our program is being quickly developed to identify and visualize families and tightly connected social groups who have had some contact with an Ebola patient."
"Tracking and containing this disease requires enormous resources. Our system can be a proactive approach to reasonably reduce the risk of exposure of Ebola spread within a community or a geographic location."
Furht is also the Director of the NSF Center for Advanced Knowledge Enablement (CAKE) at the university. Furht and his partners plan to make use of an open-source big data analysis platform called LexisNexis HPCC Systems.
According to the LexisNexis website, this system helps fifty of the top fifty banks in the U.S. to prevent crime. Retail customers and healthcare professionals are also using LexisNexis to predict and fight fraud.
The current project and research is supported by the National Science Foundation (NSF) Rapid Response Grant (RAPID), which is valued at US$400,000.
Big data analysis proves itself to be very useful for many businesses and research areas.
Computer scientists from the Queen Mary University of London and the Imperial College London recently used big data analysis to examine 17,000 songs that appeared in the U.S. Billboard Hot 100 charts from the 1960's to the year 2010.
Researchers are also using big data analysis tools to understand how human genes work in human tissues.