arXiv:2606. 02867v1 Announce Type: cross Abstract: Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging.
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.
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.
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...
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.
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.
The paper investigates how changes in prompts and persona names affect the behavior of generative agents in an epidemic simulation. It finds that synonymous prompts produce negligible differences, while minor prompt variations and contextual changes do influence outcomes. Persona names, even when imbued with distinct identities, do not significantly alter epidemic results.
arXiv:2606. 05168v1 Announce Type: cross Abstract: Training on synthetic data causes model collapse, but existing analyses treat this as single-chain degradation.
arXiv:2605. 26704v2 Announce Type: replace-cross Abstract: Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread, creating feedback loops that induce distribution shifts at policy intervention points.
arXiv:2606. 04562v1 Announce Type: new Abstract: Purpose The WHO's COVID-19 non-pharmaceutical interventions (e.
Purpose The WHO's COVID-19 non-pharmaceutical interventions (e. g.
arXiv:2202.06853v2 Announce Type: cross Abstract: To help facilitate a variety of simulations related to healthcare facilities in North Carolina, we have developed an agent-based model (ABM) to accur...