An LLM enabled real-time estimation of seasonal influenza vaccine effectiveness from social media data
Pavia, M. J.; Amaro, I. F.; Xu, D.; Gonzalez-Hernandez, G.; Scotch, M.
Show abstract
Influenza vaccine effectiveness (VE) is estimated from a limited number of clinics using a test-negative design. These standard estimates face geographic, temporal, and operational constraints. Using Twitter/X data, we applied few-shot chain-of-thought prompting to identify self-reported vaccination status and influenza test results, then implemented a test-negative-like design to estimate VE. Our estimates fell within the range of interim reports and could complement current systems, improving feasibility, timeliness, and scalability.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Zero Shot Health Trajectory Prediction Using Transformer 93%
- Enhancing Privacy-Preserving Deployable Large Language Models for Perioperative Complication Detection: A Targeted Strategy with LoRA Fine-tuning 92%
- FedWeight: Mitigating Covariate Shift of Federated Learning on Electronic Health Records Data through Patients Re-weighting 92%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.