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Mining Social Media Data for Influenza Vaccine Effectiveness Using a Large Language Model and Chain-of-Thought

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Large language models (LLMs) can now estimate influenza vaccine effectiveness (VE) in real-time by analyzing social media data. This innovative approach offers a faster, more representative public health surveillance tool.

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Area of Science:

  • Public Health
  • Computational Epidemiology
  • Artificial Intelligence

Background:

  • Influenza vaccine effectiveness (VE) estimation is crucial for public health policy.
  • Current VE estimation methods face challenges like limited data representation and reporting delays.

Purpose of the Study:

  • To explore the use of large language models (LLMs) with few-shot chain-of-thought (CoT) prompting for real-time influenza VE estimation.
  • To mine social media data for improved influenza surveillance.

Main Methods:

  • Annotated over 4,000 tweets from the 2020-2021 flu season for influenza vaccination status and test outcomes.
  • Developed and tested LLM-based CoT prompting strategies for data extraction.
  • Compared LLM performance against traditional supervised fine-tuning methods.

Main Results:

  • Achieved high inter-annotator agreement during tweet annotation.
  • The best LLM prompting strategy reached an F1 score above 87% for identifying vaccination status and test outcomes.
  • LLM approaches significantly outperformed traditional methods.

Conclusions:

  • LLM-based prompting is an effective method for extracting relevant social media information for influenza VE estimation.
  • This approach provides a valuable real-time surveillance tool complementing traditional epidemiology.
  • LLMs offer a promising avenue for enhancing public health decision-making through rapid data analysis.