# An AI-Based Public Health Data Monitoring System

arXiv: https://arxiv.org/abs/2506.04429 · HTML: https://mlsanity.com/paper/2506.04429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [Challenges of Big Data Analysis](https://mlsanity.com/paper/1308.1479/) (2013)
- [A Visual Analytics Framework for Reviewing Streaming Performance Data](https://mlsanity.com/paper/2001.09399/) (2020)
- [PIXAL: Anomaly Reasoning with Visual Analytics](https://mlsanity.com/paper/2205.11004/) (2022)
- [From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management](https://mlsanity.com/paper/2304.03368/) (2023)
- [Computationally Assisted Quality Control for Public Health Data Streams](https://mlsanity.com/paper/2306.16914/) (2023)
- [Outlier Ranking in Large-Scale Public Health Streams](https://mlsanity.com/paper/2401.01459/) (2024)

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

- [From Knowledge to Inference: Formalizing Specialized Public Health Reasoning on GlobalHealthAtlas](https://mlsanity.com/paper/2602.00491/) (2026)

## Abstract (arXiv)

Public health experts need scalable approaches to monitor large volumes of health data (e.g., cases, hospitalizations, deaths) for outbreaks or data quality issues. Traditional alert-based monitoring systems struggle with modern public health data monitoring systems for several reasons, including that alerting thresholds need to be constantly reset and the data volumes may cause application lag. Instead, we propose a ranking-based monitoring paradigm that leverages new AI anomaly detection methods. Through a multi-year interdisciplinary collaboration, the resulting system has been deployed at a national organization to monitor up to 5,000,000 data points daily. A three-month longitudinal deployed evaluation revealed a significant improvement in monitoring objectives, with a 54x increase in reviewer speed efficiency compared to traditional alert-based methods. This work highlights the potential of human-centered AI to transform public health decision-making.