# Outlier Ranking in Large-Scale Public Health Streams

arXiv: https://arxiv.org/abs/2401.01459 · HTML: https://mlsanity.com/paper/2401.01459/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## 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)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [TODS: An Automated Time Series Outlier Detection System](https://mlsanity.com/paper/2009.09822/) (2020)
- [Intrinsic Anomaly Detection for Multi-Variate Time Series](https://mlsanity.com/paper/2206.14342/) (2022)
- [Computationally Assisted Quality Control for Public Health Data Streams](https://mlsanity.com/paper/2306.16914/) (2023)

## Built on by (most cited first, 1 of 1)

- [An AI-Based Public Health Data Monitoring System](https://mlsanity.com/paper/2506.04429/) (2025)

## Abstract (arXiv)

Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks. However, they can only examine a few of the thousands of maximally-tied outliers returned by univariate outlier detection methods applied to large-scale public health data streams. To help experts distinguish the most important outliers from these thousands of tied outliers, we propose a new task for algorithms to rank the outputs of any univariate method applied to each of many streams. Our novel algorithm for this task, which leverages hierarchical networks and extreme value analysis, performed the best across traditional outlier detection metrics in a human-expert evaluation using public health data streams. Most importantly, experts have used our open-source Python implementation since April 2023 and report identifying outliers worth investigating 9.1x faster than their prior baseline. Other organizations can readily adapt this implementation to create rankings from the outputs of their tailored univariate methods across large-scale streams.