John Ayers (UC San Diego)

Real Time Research Designed to Get the Public Back in Public Health

There is now an abundance of passively generated big data — encompassing digital footprints left on electronic devices and online (including search engines, social networking sites, mobile devices, websites, etc.) — that can be analyzed to yield instantaneous health insights. Yet, the full potential of these data will not be realized because the research enterprise that governs what public health research is prioritized needs to catch up to advances in data availability and data science. In this talk a framework for the future of public health research designed around real time insights will be presented.

About the Speaker

John W. Ayers is a Johns Hopkins and Harvard trained computational epidemiologist who uses big data to yield rapid and novel insights with measurable public health impacts. Together with Eric Leas, Alicia Nobles, and Mark Dredze he has yielded many actionable insights including the discovery of seasonal patterns across many types of mental illness, the discovery of circaseptan (day of the week) patterns in health behaviors, the first replicable evaluation strategy for discrete awareness campaigns, a major overhaul of infectious disease forecasting algorithms, the first real-time linkages between macroeconomic conditions and typically unreported mental health issues, and a data driven strategy to passively discover the public’s health concerns. His work is frequently cited in the media, even including Saturday Night Live, and he is widely celebrated for pushing the boundaries of research, including being named a “brave scientist” by Nate Silver’s Five Thirty Eight and “innovator” by the Burroughs Wellcome Foundation.