# Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US

arXiv: https://arxiv.org/abs/2005.00475 · HTML: https://mlsanity.com/paper/2005.00475/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 3 of 3)

- [Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset](https://mlsanity.com/paper/2103.01196/) (2021)
- [COVID-19 Tweets Analysis through Transformer Language Models](https://mlsanity.com/paper/2103.00199/) (2021)
- [Machine Translation for Accessible Multi-Language Text Analysis](https://mlsanity.com/paper/2301.08416/) (2023)

## Abstract (arXiv)

During a disease outbreak, timely non-medical interventions are critical in preventing the disease from growing into an epidemic and ultimately a pandemic. However, taking quick measures requires the capability to detect the early warning signs of the outbreak. This work collects Twitter posts surrounding the 2020 COVID-19 pandemic expressing the most common symptoms of COVID-19 including cough and fever, geolocated to the United States. Through examining the variation in Twitter activities at the state level, we observed a temporal lag between the rises in the number of symptom reporting tweets and officially reported positive cases which varies between 5 to 19 days.