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.

翻译：流行病多智能体模型（ABM）能够编码行为与政策的异质性，但其计算速度过慢，无法满足医院夜间规划需求。我们开发了可直接从百亿亿级ABM轨迹中学习的县级就绪代理模型，该模型采用通用微分方程（UDE）框架：即具有神经参数化接触率$κ_φ(u,t)$的机制性SEIR族常微分方程（无加性残差）。我们的贡献包含三个方面：我们采用多重打靶法和基于观测器的预测误差方法（PEM）来稳定神经增强流行病动力学在干预驱动机制转换中的辨识过程；我们强制保持正定性与质量守恒特性，并证明所学习的接触率参数化可产生适定的向量场；我们针对ABM集成与UDE基线量化了准确性、校准能力与计算性能。在代表性ExaEpi场景中，PEM-UDE相较于单次打靶UDE将均方误差降低了77%（3.00对比13.14），相较于MS-UDE降低了20%（3.75对比3.00）。可靠性同步提升：ABM的$10$-$90$%与$25$-$75$%置信带经验覆盖率从UDE的0.68/0.43和MS-UDE的0.79/0.55，分别提升至PEM-UDE的0.86/0.61以及MS+PEM-UDE的0.94/0.69，表明其具备校准化的不确定性估计而非过度自信的拟合。推断过程在商用CPU上仅需数秒（每项约90天的预测耗时20-35秒），支持在笔记本电脑上进行夜间"假设分析"扫描。相较于约100CPU小时的ABM基准运行，该方法使单场景耗时降低约$10^{4}$倍。这弥合了现实性与时效性之间的鸿沟，支持阈值感知决策（如维持ICU占用率$<75$%），保持机制可解释性，并能在标准机构硬件上实现校准化、风险感知的场景规划。超越流行病学领域，ABM$\to$UDE方案为将多智能体模拟器提炼为快速可靠的代理模型提供了可移植路径，可拓展至其他科学领域。