Large language model-generated patterns for epidemiological modeling: a multi-benchmark evaluation framework
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
arXiv:2606. 02867v1 Announce Type: cross Abstract: Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging.
The paper presents a pipeline that uses Large Language Models (LLMs) to extract information from 536 peer‑reviewed agent‑based modeling papers for systematic literature reviews (SLRs). GPT‑4.1 achieves about 77.95% paper‑level accuracy, while GPT‑5.0 reaches 81.67%. Field‑level accuracy varies widely, and the study notes that agreement between LLMs can signal output quality, with low agreement indicating hallucinations and high agreement with low accuracy suggesting noise in the human dataset.
The article introduces EpidemIA, a training‑free AI Multi Agent System that lets users interact with epidemiological models and perform statistical analyses through natural language conversations. Built on a single‑GPU workstation, it incorporates a modular suite of expert‑written models and an open‑weights LLM to keep data local. Using Emergency Department visit data from IRCCS Sant'Orsola, the system demonstrates both simple statistical queries and complex dynamical predictions via natural language.
arXiv:2610.02744v1 Announce Type: new Abstract: Epidemic intervention policies are textual artefacts that human decision-makers interpret, justify, and revise through natural language, making large l...
The paper presents EpidemIA, a training‑free AI Multi Agent System that lets clinicians and administrators use epidemiological models and perform statistical analyses through natural language conversations. The system runs on a single‑GPU workstation, employs a locally hosted open‑weights LLM to keep data private, and includes a modular suite of expert‑written models. Using Emergency Department visit data from IRCCS Sant’Orsola, the authors demonstrate that users can conduct both simple statistical queries and complex dynamical predictions via natural language.
arXiv:2608. 15689v1 Announce Type: cross Abstract: This study presents a hybrid epidemiological and behavioural framework to simulate the spread of health misinformation on social media.