Delphi publishes millions of public-health-related data points per day, including the total number of daily influenza cases, hospitalizations, and deaths per county and state in the United States (US). This data helps public health practitioners, data professionals, and members of the public make important, informed decisions relating to health and well-being.
Yet, as data volumes continue to grow quickly (Delphi’s data volume expanded 1000x in just 3 years), it is infeasible for data reviewers to inspect every one of these data points for subtle changes in
These issues, if undetected, can have critical downstream ramifications for data users (as shown by the example in Fig 1).
We care about finding data issues like these so that we can alert downstream data users accordingly. That is why our goal in the FlaSH team (Flagging Anomalies in Streams related to public Health) is to quickly identify data points that warrant human inspection and create tools to support data review. Towards this goal, our team of researchers, engineers, and data reviewers iterate on our deployed interdisciplinary approach. In this blog series, we will cover the different methods and perspectives of the FlaSH project.
Members: Ananya Joshi, Nolan Gormley, Richa Gadgil, Tina Townes
Former Members: Luke Neurieter, Katie Mazaitis
Advisors: Peter Jhon, Roni Rosenfeld, Bryan Wilder