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.
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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.
The Epydemix Agent Framework adds an AI‑friendly layer to the Epydemix Python library for stochastic epidemic modeling. It provides four key features: model and parameter discovery, declarative scenario validation, execution via tested code, and result inspectability, enabling an AI agent to manage the entire modeling workflow from natural‑language input to quantitative outputs. The authors demonstrate the framework with a vaccination strategy case study and evaluate it over 50 agent sessions, showing reductions in turns, output tokens, and cost compared to direct Python use.
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:2608.29535v1 Announce Type: cross Abstract: Infectious disease transmission is shaped by patterns of human interaction, which adapt as epidemic conditions change. Capturing these context-depend...
arXiv:2606. 02867v1 Announce Type: cross Abstract: Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging.
arXiv:2607. 05999v1 Announce Type: new Abstract: LLM-agent simulations make natural-language social scenarios easy to instantiate, but their outputs can be overread as predictions and are often difficult to compare with explicit social dynamics.