Big data derived from electronic health records, social media, the internet and other digital sources have the potential to provide more timely and detailed information on infectious disease threats or outbreaks than traditional surveillance methods. A team of scientists led by the National Institutes of Health reviewed the growing body of research on the subject and has published its analyses in a special issue of The Journal of Infectious Diseases (link is external).

“The ultimate goal is to be able to forecast the size, peak or trajectory of an outbreak weeks or months in advance…”

— Cecile Viboud, Ph.D., Senior Scientist, FIC

Traditional infectious disease surveillance — typically based on laboratory tests and other data collected by public health institutions — is the gold standard. But, the authors note it can have time lags, is expensive to produce, and typically lacks the local resolution needed for accurate monitoring. Further, it can be cost-prohibitive in low-income countries. In contrast, big data streams from internet queries, for example, are available in real time and can track disease activity locally, but have their own.

Each article features a promising example of the use of big data to monitor and model infectious diseases activity:

• In the United States, researchers found what they describe as “excellent alignment” between medical insurance claim data for flu-like illnesses and proven influenza activity reported by the Centers for Disease Control and Prevention.
• A European surveillance system that began collecting crowdsourced data on influenza as part of a research project is now considered an adjunct to existing surveillance activities. Influenzanet (link is external) uses standardized online surveys to gather information from volunteers who self-report their symptoms on a weekly basis. A number of European Union member states are now using the tool and expanding it to include Zika, salmonella and other diseases.
• An online platform, ResistanceOpen (link is external), was developed by U.S. and Canadian scientists to monitor antibiotic resistance at the regional level. The site takes advantage of publicly available, online data from community healthcare institutions as well as regional, national and international bodies. An analysis showed online information compared favorably with traditional reporting systems in the two countries.

While the new hybrid models that combine traditional and digital disease surveillance methods show promise, the scientists agree there is still an overall scarcity of reliable surveillance information, especially compared to other fields such as climatology, where the data sets are huge. “To be able to produce accurate forecasts, we need better observational data that we just don’t have in infectious diseases,” notes Professor Shweta Bansal of Georgetown University, a co-editor of the supplement. “There’s a magnitude of difference between what we need and what we have, so our hope is that big data will help us fill this gap.”

Multi-disciplinary initiatives such as the NIH-led Big Data to Knowledge program will be instrumental in expanding the use of big data in research, as noted in the supplement.

The publication’s authors include scientists affiliated with Fogarty’s Research and Policy for Infectious Diseases program (RAPIDD), grantees from NIH’s National Institute of General Medical Sciences, and researchers from nearly 20 universities throughout North America and Europe. The supplement was produced with support from Georgia State University, the Fogarty International Center, Northeastern University and Georgetown University.

About the Fogarty International Center
The Center addresses global health challenges through innovative and collaborative research and training programs, and supports and advances the NIH mission through international partnerships. For more information, visit www.fic.nih.gov.