  Irregularities in public health data streams (like COVID-19 Cases) hamper
data-driven decision-making for public health stakeholders. A real-time,
computer-generated list of the most important, outlying data points from
thousands of daily-updated public health data streams could assist an expert
reviewer in identifying these irregularities. However, existing outlier
detection frameworks perform poorly on this task because they do not account
for the data volume or for the statistical properties of public health streams.
Accordingly, we developed FlaSH (Flagging Streams in public Health), a
practical outlier detection framework for public health data users that uses
simple, scalable models to capture these statistical properties explicitly. In
an experiment where human experts evaluate FlaSH and existing methods
(including deep learning approaches), FlaSH scales to the data volume of this
task, matches or exceeds these other methods in mean accuracy, and identifies
the outlier points that users empirically rate as more helpful. Based on these
results, FlaSH has been deployed on data streams used by public health
stakeholders.
