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PreprintCC BY-NC-NDmedRxiv

An LLM enabled real-time estimation of seasonal influenza vaccine effectiveness from social media data

  • M. J. Pavia
  • I. F. Amaro
  • D. Xu
  • G. Gonzalez-Hernandez
  • M. Scotch1
  1. 1Arizona State University
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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.

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Cite

BibTeXRIS
BibTeX
@misc{pavia2026enabled,
  title = {{An LLM enabled real-time estimation of seasonal influenza vaccine effectiveness from social media data}},
  author = {M. J. Pavia and I. F. Amaro and D. Xu and G. Gonzalez-Hernandez and M. Scotch},
  howpublished = {medRxiv},
  year = {2026},
  month = aug,
  doi = {10.64898/2026.08.28.26361670},
  url = {https://www.medrxiv.org/content/10.64898/2026.08.28.26361670v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - An LLM enabled real-time estimation of seasonal influenza vaccine effectiveness from social media data
AU  - M. J. Pavia
AU  - I. F. Amaro
AU  - D. Xu
AU  - G. Gonzalez-Hernandez
AU  - M. Scotch
PY  - 2026
DA  - 2026/08/28
PB  - medRxiv
DO  - 10.64898/2026.08.28.26361670
UR  - https://www.medrxiv.org/content/10.64898/2026.08.28.26361670v1
AB  - 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.
ER  -