Although laboratory tests and other data collected by public health institutions have historically been the gold standard for infectious disease surveillance, these traditional methods of collecting information are falling short.
Instead, big data drawn from electronic health records, social media, the Internet and other digital data are emerging as potentially more timely and detailed information sources for helping to combat outbreaks of infectious diseases, say a team of scientists led by the National Institutes of Health.
Writing in a special issue of The Journal of Infectious Diseases, researchers contend that the time has come for public health to finally embrace big data.
“While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution,” states an opinion piece. “A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens.”
However, rather than calling for replacing traditional surveillance systems with big data sources, the authors advocate for increased use of hybrid systems which combine the two methods, which they believe is the most promising option moving forward for surveillance and modeling.
“The ultimate goal is to be able to forecast the size, peak or trajectory of an outbreak weeks or months in advance in order to better respond to infectious disease threats. Integrating big data in surveillance is a first step toward this long-term goal,” says Cecile Viboud, co-editor of the 10-article supplement and a senior scientist at the NIH’s Fogarty International Center.
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