Special Issue on Big Data for Infectious Disease Surveillance and Modeling
Scientists are constantly looking for new ways to make technology work to our advantage. Professor Shweta Bansal and a team of epidemiologists, computer scientists, and mathematical modelers led by the NIH have been looking at innovative methods for infectious disease surveillance that combines traditional tools with new big data sets. Professor Bansal was recently a co-editor for a special supplemental issue of the Journal of Infectious Disease-focused on exactly that topic. Through its 10 articles, the special issue evaluated the opportunities and challenges associated with several sources of big data: medical encounter files, crowdsourced data from self-reporting volunteers, and social media, the internet, and mobile phones. The authors explain that these new hybrid models, while they have great potential, also require a great deal of development. But the muti-disciplinary approach that Bansal and her colleagues have highlighted is a big step towards new and effective means of infectious disease surveillance and control.
Global Infectious Diseases Ph.D. candidate Elizabeth Lee was also the lead author on an article in this special issue, focusing on spatial big data and their potential for filling geographical information gaps, and the technical, practical and privacy challenges that must be addressed for their effective use in disease surveillance and modeling.