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.
arXiv:2601.03471v4 Announce Type: replace-cross Abstract: Reliable epidemiological reasoning requires synthesizing study evidence to infer disease burden, transmission dynamics, and intervention effe...
arXiv:2606. 06360v1 Announce Type: new Abstract: Modelling individual decision-making during infectious disease outbreaks is crucial for understanding behavioural dynamics and informing effective public health interventions.
ReviewPilot is a conversational multi‑agent system designed to automate the PRISMA workflow for systematic literature reviews, covering both paper screening and evidence extraction. Researchers can customize screening criteria and extraction schemas through dialogue, with a project memory preserving configurations and revisions. The system records the rationale for each screening decision and the source of every extracted value, and it has demonstrated superior performance to single‑prompt LLMs and AgentSLR in epidemiology, achieving screening macro F1 scores up to 0.776 and extraction F1 up to 0.788, while also showing close agreement with human annotators in biomedicine and human‑computer interaction case studies.
Modelling individual decision-making during infectious disease outbreaks is crucial for understanding behavioural dynamics and informing effective public health interventions. Prior work has shown that large language models can simulate realistic human behaviour by generating agent decisions based on demographic prompts and situational context.
arXiv:2607. 06757v1 Announce Type: new Abstract: Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making.
arXiv:2601. 07988v2 Announce Type: replace-cross Abstract: While NLP typically treats documents as independent and unordered samples, in longitudinal studies, this assumption rarely holds: documents are nested within authors and ordered in time, forming person-indexed, time-ordered $\textit{behavioral sequences}$.