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EpiLens: Lightweight RAG for Epidemiological Knowledge Acquisition

Vincenzo Perri, Ciro Cattuto, Daniela Paolotti · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26730 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

Beyogenの将来の噴火に備えるために、研究者はどのような研究が行われたか、どのようなデータを使用しているかを知る必要がある。大規模な文献を手でマッピングすることは遅いが、大規模な言語モデルは単独で源を混乱させたり混乱させることができる。我々はEpiLensを紹介し、軽量でオープンソースのリリース増加型世代ワークフローをEpiLensが214の手動でラベル付けされた書類に対する固定リリーステンプレートと出力スケジュールを適用する。任務特有のトレーニングデータなしに、分類器は4つの任務の間で監督され、ゼロショットベースラインを超える。我々は2297の書類を複数の病原体パラメータ研究

Machine translation (Meta M2M100); the English original follows.

Abstract

To prepare for future outbreaks, researchers need to know what studies have been done and what data they use. Mapping a large literature by hand is slow, while large language models alone can misstate or conflate sources. We present EpiLens, a lightweight, open-source retrieval-augmented generation package that applies a fixed retrieval template and output schema to every paper in a corpus and links outputs to source passages. We build a corpus of 2,297 papers from the sources of a multi-pathogen parameter study and evaluate EpiLens's paper-classification workflow against 214 manually labelled papers. Without task-specific training data, the classifier matches or exceeds the tested supervised and zero-shot baselines across four tasks. The study's early extraction sheets cite published sources for 327 estimates. We link each estimate to a cited document or to corpus studies included by a review. Then, we use the classifier's labels to compare the papers associated with different parameters and pathogens. EpiLens labels more papers linked to reproduction numbers than fatality risks as model-based (54% versus 22%, compared with 62% versus 24% in the manual labels). The gap is consistent with the frequent use of transmission models to estimate reproduction numbers and persists within each of five pathogens compared. Beyond the contrast between parameters, the model-based share of papers linked to incubation-period estimates differs by pathogen: 55% for SARS-CoV-2 versus 6% for mpox.

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Topics: Computer science, Workflow, Classifier (UML), Artificial intelligence, Schema (genetic algorithms), Training set

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