Omicron detection with large language models and YouTube audio data

Area of Science:

• Digital Health
• Artificial Intelligence
• Public Health

Background:

• Publicly available audio data offers a novel resource for developing digital health technologies.
• Large language models (LLMs) show promise in analyzing complex datasets for health applications.

Approach:

• Collected and transcribed YouTube audio data from individuals with self-declared COVID-19, upper respiratory infections (URI), and healthy controls.
• Utilized LLMs to detect self-reported COVID-19 cases, differentiate from other respiratory illnesses, and classify COVID-19 variants based on described symptoms.
• Employed the Whisper model for transcription and optimized prompts for LLM analysis.

Key Points:

• LLMs achieved 0.89 accuracy in identifying self-reported COVID-19 cases and 0.97 accuracy in distinguishing them from other respiratory illnesses.
• LLMs demonstrated a 0.77 mean accuracy in classifying COVID-19 variants using only symptom descriptions from audio data.
• This research contrasts with prior studies by using unscripted, real-world audio data from public online sources.

Conclusions:

• Publicly available audio data, analyzed by LLMs, presents a viable method for identifying COVID-19 and related respiratory conditions.
• This approach offers a new paradigm for pandemic management tools, highlighting the utility of audio data in clinical and public health surveillance.
• The findings underscore the potential of leveraging everyday online audio for scalable and accessible health monitoring.