Using iterative agentic retrieval-augmented generation to refine the risk of infection from social contexts during the COVID-19 pandemic: Development and Evaluation Study

Background: Contact tracing is crucial to limit pathogen transmission and to gather epidemiological data. However, despite machine learning optimizations, it remains time-consuming. Large language models have been used for infection risk prediction, but no existing application converses with a user to adapt its risk estimates. Objective: We aimed to develop an iterative retrieval-augmented generation conversational framework to predict coronavirus disease 2019 (COVID-19) infection risk from textual social data. The conversational agent aims to gather personalized risk factors based on descript

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