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

• Published in: Nature communications Vol. 16; no. 1; pp. 5836 - 13
• Main Authors: Bizel-Bizellot, Gaston, Galmiche, Simon, Lelandais, Benoît, Charmet, Tiffany, Coudeville, Laurent, Fontanet, Arnaud, Zimmer, Christophe
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/326/596/4130; 692/499; 692/699/255; 692/700/478/174; Adult; Clusters; Computation and Language; Computer Science; COVID-19; COVID-19 - epidemiology; COVID-19 - transmission; Disease transmission; Document and Text Processing; Epidemiology; Female; France - epidemiology; Households; Humanities and Social Sciences; Humans; Infections; Infectious diseases; Language; Large Language Models; Life Sciences; Male; Middle Aged; multidisciplinary; Natural language processing; Pandemics; Polls & surveys; Public health; Questionnaires; Questions; Santé publique et épidémiologie; SARS-CoV-2; Science; Science (multidisciplinary); Severe acute respiratory syndrome coronavirus 2; Sociodemographics; Surveys; Surveys and Questionnaires; Viral diseases; France

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 na...