When a disease outbreak occurs, often the panic that ensues is worse than the disease itself. To that end, a team of researchers have developed a computer model to forecast overreactions
The researchers, from MIT, Draper Laboratory, and Ascel Bio, wrote about their research in a paper published in the journal Interface. Big data was the main ingredient in the project, according to an article in MIT News.
Researchers looked at data from hospitals, social media and news reports, among other sources. They compared the social media posts and news articles to data from hospital records about incidences of the disease, according to the article.
The outbreaks used in the research were the 2009 spread of H1N1 and the 2003 spread of SARS. They found that most of the time the reaction to the risk far outweighs the risk itself.
They are now also using the tool to do a study of the recent Ebola outbreak.
"I hope in the future, if we could predict that these bad social and economic consequences are going to happen that might cost a lot of money and might cost a lot of lives, that people can take measures to counteract these effects," report co-author Marta Gonzalez, an assistant professor of civil and environmental engineering at MIT, says in the article.
Healthcare technology and big data also are being used to track, not just reaction to outbreaks, but also when and where the outbreaks themselves might occur.
Researchers at Los Alamos National Laboratory in New Mexico say that tracking Wikipedia page views can forecast the spread of influenza and dengue fever.
And Google Flu Trends, while hitting some bumps in the road, serves as a good "baseline indicator" of epidemic trends.
Tools like these are especially important in the U.S., which is woefully underprepared for potential future disease outbreaks, according to report from the Trust for America's Health and Robert Wood Johnson Foundation.