  Introduction: The COVID-19 pandemic highlighted the importance of making
epidemiological data and scientific insights easily accessible and explorable
for public health agencies, the general public, and researchers.
State-of-the-art approaches for sharing data and insights included regularly
updated reports and web dashboards. However, they face a trade-off between the
simplicity and flexibility of data exploration. With the capabilities of recent
large language models (LLMs) such as GPT-4, this trade-off can be overcome.
  Results: We developed the chatbot "GenSpectrum Chat"
(https://cov-spectrum.org/chat) which uses GPT-4 as the underlying large
language model (LLM) to explore SARS-CoV-2 genomic sequencing data. Out of 500
inputs from real-world users, the chatbot provided a correct answer for 453
prompts; an incorrect answer for 13 prompts, and no answer although the
question was within scope for 34 prompts. We also tested the chatbot with
inputs from 10 different languages, and despite being provided solely with
English instructions and examples, it successfully processed prompts in all
tested languages.
  Conclusion: LLMs enable new ways of interacting with information systems. In
the field of public health, GenSpectrum Chat can facilitate the analysis of
real-time pathogen genomic data. With our chatbot supporting interactive
exploration in different languages, we envision quick and direct access to the
latest evidence for policymakers around the world.
