Leveraging Large Language Models for Infectious Disease Surveillance-Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis.

ABSTRACT:

Background

The emergence of new SARS-CoV-2 variants, the resulting reinfections, and post-COVID-19 condition continue to impact many people's lives. Tracking websites like the one at Johns Hopkins University no longer report the daily confirmed cases, posing challenges to accurately determine the true extent of infections. Many COVID-19 cases with mild symptoms are self-assessed at home and reported on social media, which provides an opportunity to monitor and understand the progression and evolving trends of the disease.

Objective

We aim to build a publicly available database of COVID-19-related tweets and extracted information about symptoms and recovery cycles from self-reported tweets. We have presented the results of our analysis of infection, reinfection, recovery, and

SUBMITTER: Xie J 

PROVIDER: S-EPMC11888100 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature