Keeping track of disease trends such as influenza outbreaks has the potential to be far quicker and less costly by monitoring a social network program such as Twitter than following the traditional methods of disease surveillance, according to a computer science expert at Southeastern Louisiana University.
A process called syndromic surveillance uses collected health-related data to alert health officials to the probability of an outbreak of disease, typically influenza or other contagious diseases. The technique involves collecting data from hospitals, clinics and other sources, a labor-intensive and time consuming approach. By monitoring a social network such as Twitter, researchers can capture comments from people with the flu who are sending out status messages.
“A micro-blogging service such as Twitter is a promising new data source for Internet-based surveillance because of the volume of messages, their frequency and public availability,” said Aron Culotta, assistant professor of computer science. “This approach is much cheaper and faster than having thousands of hospitals and health care providers fill out forms each week.
“The Centers for Disease Control produces weekly estimates,” he added, “but those reports typically lag a week or two behind. This approach produces estimates daily.”
Culotta and two student assistants analyzed more than 500 million Twitter messages over the eight-month period of August 2009 to May 2010, collected using Twitter’s application programming interface (API). By using a small number of keywords to track rates of influenza-related messages on Twitter, the team was able to forecast future influenza rates.
“Once the program is running, it’s actually neither time consuming nor expensive,” he said. “It’s entirely automated because we’re running software that samples each day’s messages, analyzes them and produces an estimate of the current proportion of people with the flu.”
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