用神经网络加速疫情模拟，每晚可跑数千种预案。
ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning
- 用神经网络替代复杂疫情模型中的接触率，实现快速预测。
- 预测误差降低77%，不确定性估计更可靠，真实覆盖率超90%。
- 在普通电脑上几秒出结果，适合医院每日决策参考。
基于代理的流行病模型（ABMs）能刻画行为与政策差异，但计算太慢，难以支持每晚医院规划。本文提出面向县区的代理模型，直接从百亿级规模的ABM轨迹中学习，采用通用微分方程（UDEs）：以机制性SEIR类常微分方程为基础，用神经网络参数化接触率κ_ϕ(u,t)，不加残差项。贡献包括：引入多段射击法与基于观测器的预测误差方法（PEM），稳定应对干预措施引发的动态突变；强制保证正值性与质量守恒，确保学习到的向量场合理；并通过与ABM集合及基线UDE对比，量化了精度、校准性与计算效率。在典型ExaEpi场景下，PEM-UDE相比单段射击UDE均方误差降低77%（3.00 vs. 13.14），比多段射击UDE降低20%（3.75）。置信区间覆盖率显著提升：10%-90%与25%-75%带状区间实测覆盖率从UDE的0.68/0.43、MS-UDE的0.79/0.55，上升至PEM-UDE的0.86/0.61和MS+PEM-UDE的0.94/0.69，表明不确定性校准而非过度自信。单次预测仅需20-35秒，可在普通CPU上完成约90天的夜间‘情景推演’。相比约100小时的ABM参考运行，每场景节省约10⁴倍时钟时间。该方法弥合了现实性与时效性差距，支持阈值敏感决策（如保持ICU占用<75%），保留机制可解释性，并在标准机构硬件上实现可校准的风险感知规划。该方法可推广至其他科学领域，为将基于代理的模拟器转化为快速可信代理提供通用路径。
原文摘要 · Abstract (English)
Agent-based epidemic models (ABMs) encode behavioral and policy heterogeneity but are too slow for nightly hospital planning. We develop county-ready surrogates that learn directly from exascale ABM trajectories using Universal Differential Equations (UDEs): mechanistic SEIR-family ODEs with a neural-parameterized contact rate $κ_ϕ(u,t)$ (no additive residual). Our contributions are threefold: we adapt multiple shooting and an observer-based prediction-error method (PEM) to stabilize identification of neural-augmented epidemiological dynamics across intervention-driven regime shifts; we enforce positivity and mass conservation and show the learned contact-rate parameterization yields a well-posed vector field; and we quantify accuracy, calibration, and compute against ABM ensembles and UDE baselines. On a representative ExaEpi scenario, PEM-UDE reduces mean MSE by 77% relative to single-shooting UDE (3.00 vs. 13.14) and by 20% relative to MS-UDE (3.75). Reliability improves in parallel: empirical coverage of ABM $10$-$90$% and $25$-$75$% bands rises from 0.68/0.43 (UDE) and 0.79/0.55 (MS-UDE) to 0.86/0.61 with PEM-UDE and 0.94/0.69 with MS+PEM-UDE, indicating calibrated uncertainty rather than overconfident fits. Inference runs in seconds on commodity CPUs (20-35 s per $\sim$90-day forecast), enabling nightly ''what-if'' sweeps on a laptop. Relative to a $\sim$100 CPU-hour ABM reference run, this yields $\sim10^{4}\times$ lower wall-clock per scenario. This closes the realism-cadence gap, supports threshold-aware decision-making (e.g., maintaining ICU occupancy $<75$%), preserves mechanistic interpretability, and enables calibrated, risk-aware scenario planning on standard institutional hardware. Beyond epidemics, the ABM$\to$UDE recipe provides a portable path to distill agent-based simulators into fast, trustworthy surrogates for other scientific domains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。