Leveraging Large Language Models for Infectious Disease Surveillance—Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis
Although with some biases and limitations, self-reported tweet data serves as a valuable complement to clinical data, especially in the postpandemic era dominated by mild cases. Our web-based analytic platform can play a significant role in continuously tracking COVID-19, finding new uncommon symptoms, detecting and monitoring the manifestation of long-term effects, and providing necessary insights to the public and decision-makers.
Authors
- Xiu‐Feng Wan (ORCID: https://orcid.org/0000-0003-2629-9234)
- Guanghui An (ORCID: https://orcid.org/0000-0002-3437-5834)
- Dong Xu (ORCID: https://orcid.org/0000-0002-4809-0514)
- Jiacheng Xie (ORCID: https://orcid.org/0000-0001-9491-1216)
- Joel Hilliard
- Ziyang Zhang (ORCID: https://orcid.org/0000-0003-0541-7211)
- Shuai Zeng (ORCID: https://orcid.org/0000-0001-7632-427X)
- Xiaoting Tang (ORCID: https://orcid.org/0000-0002-6635-1044)
- Lei Jiang (ORCID: https://orcid.org/0000-0002-6365-1965)
- Yang Yu (ORCID: https://orcid.org/0009-0006-1475-7487)
Institutions
- Shanghai University of Traditional Chinese Medicine (CN)
- National Influenza Center (MN)
- Community Health Center (US)
- University of Missouri (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-01-16
- DOI
- https://doi.org/10.2196/63190
- Citations
- 4
- Primary Topic
- Misinformation and Its Impacts
- Type
- article
- Field-Weighted Citation Impact
- 16.81