Extracting circumstances of Covid-19 transmission from free text with large language models.

ABSTRACT: Identifying the circumstances of transmission of an emerging infectious disease rapidly is central for mitigation efforts. Here, we explore how large language models (LLMs) can automatically extract such circumstances from free-text descriptions in online surveys, in the context of Covid-19. In a nationwide study conducted online in France, we enrolled 545,958 adults with recent SARS-CoV-2 infection and inquired about the circumstances of transmission in both closed-ended and open-ended questions. First, we trained a classification model based on a pretrained LLM to predict one of seven predefined infection contexts (Work, Family, Friends, Sports, Cultural, Religious, Other) from the free text in answers to open-ended questions. We achieved an unbalanced accuracy of 75%, which increased to

SUBMITTER: Bizel-Bizellot G 

PROVIDER: S-EPMC12219669 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature