Introducing FlaSH, Part 1: Meet the Team

Introducing FlaSH, Part 1: Meet the Team

Ananya Joshi, Nolan Gormley, Richa Gadgil, Tina Townes


    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

    • quality (like those resulting from data delays) or
    • disease dynamics (like an outbreak).

    These issues, if undetected, can have critical downstream ramifications for data users (as shown by the example in Fig 1).

    Fig 1. Data quality changes in case counts, shown by the large spikes in March and July 2022, when cases were trending down, resulted in similar spikes for predicted counts (red) from multiple forecasts that were then sent to the US CDC. A weekly forecast per state, for cases, hospitalizations, and deaths, up to 4 weeks in the future means that modeling teams would have to review 600 forecasts per week and may not have been able to catch the upstream data issue.

    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

    Ananya Joshi is a Ph.D. student in the Computer Science Department and is a member of Delphi supported by the NSF GRFP.
    Nolan Gormley is a Research Programmer at Delphi who works on the Epidata API and has been specializing in data quality monitoring and response.
    Richa Gadgil is a masters student in the Machine Learning Department.
    Tina Townes is a member of the Delphi group specializing in data quality monitoring and response.
    © 2024 Delphi group authors. Text and figures released under CC BY 4.0 ; code under the MIT license.

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