# Computationally Assisted Quality Control for Public Health Data Streams

arXiv: https://arxiv.org/abs/2306.16914 · HTML: https://mlsanity.com/paper/2306.16914/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?](https://mlsanity.com/paper/2105.07844/) (2021)
- [Data-Driven Decision Making in COVID-19 Response: A Survey](https://mlsanity.com/paper/2202.11435/) (2022)

## Built on by (most cited first, 2 of 2)

- [Outlier Ranking in Large-Scale Public Health Streams](https://mlsanity.com/paper/2401.01459/) (2024)
- [An AI-Based Public Health Data Monitoring System](https://mlsanity.com/paper/2506.04429/) (2025)

## Abstract (arXiv)

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.