# Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective

arXiv: https://arxiv.org/abs/1903.05801 · HTML: https://mlsanity.com/paper/1903.05801/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [A Comprehensive Survey of Text Classification Techniques and Their Research Applications: Observational and Experimental Insights](https://mlsanity.com/paper/2401.12982/) (2024)
- [Applications and Challenges of Sentiment Analysis in Real-life Scenarios](https://mlsanity.com/paper/2301.09912/) (2023)
- [Relation Extraction from News Articles (RENA): A Tool for Epidemic Surveillance](https://mlsanity.com/paper/2311.01472/) (2023)
- [Intelligent Stress Assessment for e-Coaching](https://mlsanity.com/paper/2311.03385/) (2023)
- [RACCOON: A Retrieval-Augmented Generation Approach for Location Coordinate Capture from News Articles](https://mlsanity.com/paper/2501.11440/) (2025)

## Abstract (arXiv)

Epidemic intelligence deals with the detection of disease outbreaks using formal (such as hospital records) and informal sources (such as user-generated text on the web) of information. In this survey, we discuss approaches for epidemic intelligence that use textual datasets, referring to it as `text-based epidemic intelligence'. We view past work in terms of two broad categories: health mention classification (selecting relevant text from a large volume) and health event detection (predicting epidemic events from a collection of relevant text). The focus of our discussion is the underlying computational linguistic techniques in the two categories. The survey also provides details of the state-of-the-art in annotation techniques, resources and evaluation strategies for epidemic intelligence.