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. 02867v1 Announce Type: cross Abstract: Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging.
Graph World Models for Constrained Epidemic Policy Planning introduces EpiMind, a graph-factored recurrent state-space model that generates joint policy-conditioned rollouts from regional latent beliefs. The framework uses graph-temporal ADMM to optimize regional interventions, enforce shared-resource feasibility, and evaluate temporal specifications under the learned model. EpiMind achieves a 29% reduction in admission RMSE compared to graph-free dynamics, plans within 1–5% of the best feasible constant policy while guaranteeing shared-budget feasibility, and outperforms all deployable baselines across three resource budgets in real-context evaluation.
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:2606. 05692v1 Announce Type: new Abstract: Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes.
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.
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.
Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes. Existing datasets either rely on real-world observations without ground-truth counterfactuals or on simplified simulations that fail to capture complex causal dynamics.
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: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:2606.07399v3 Announce Type: replace-cross Abstract: Generative models are increasingly used to support decision-making in complex systems, where interventions may be joint and high-dimensional,...