Papers for

epidemiological modelers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Large language models show mixed accuracy in predicting behaviors for disease spread

Large language model-generated patterns for epidemiological modeling: a multi-benchmark evaluation framework

Abstract: Representing individual-level behavioral heterogeneity is challenging in epidemiological modeling, particularly when empirical behavioral data are sparse. We assess the potential of large language model (LLM)-generated behavioral outputs in addressing this challenge, using hepatitis C virus-related behaviors among people who inject drugs as a case study. Correspondence with epidemiological patterns was evaluated using external benchmarks: literature-informed association directions, correlations from an agent-based model (ABM), and survey associations. Across three LLMs, unconditional directional recovery across 28 retained literature-informed associations was 46-68%, compared with 82-93% agreement when LLMs were directly asked to classify the same associations. Mean absolute differences from ABM and survey correlation patterns were 0.11-0.13 and 0.15-0.18, respectively. Together, these findings show partial alignment between LLM-generated behavioral relationships and epidemiological evidence, despite higher knowledge-probe agreement. More systematic evaluation is needed before incorporating LLM-generated behavioral outputs into epidemiological models that simulate transmission dynamics.

Sun 11 OctArtificial Intelligence
The gist
It can be hard to represent different people’s behaviors in disease models when real data are limited. The authors tested if large language models (LLMs), which generate text from patterns they learned, can predict behaviors related to hepatitis C among drug users. They found LLMs somewhat matched known behavior patterns but not perfectly, and performed better when asked more direct questions about those patterns. This means LLMs might help simulate behaviors for disease spread, but more testing is needed before they can be fully trusted for this use.
Open → 2610.15994v1