During the COVID-19 crisis, mechanistic models have guided evidence-based decision making. However, time-critical decisions in a dynamical environment limit the time available to gather supporting evidence. We address this bottleneck by developing a graph neural network (GNN) surrogate of an age-structured and spatially resolved mechanistic metapopulation simulation model. This combined approach complements classical modeling approaches which are mostly mechanistic and purely data-driven machine learning approaches which are often black box. Our design of experiments spans outbreak and persistent-threat regimes, up to three contact change points, and age-structured contact matrices on a spatial graph with 400 nodes representing German counties. We benchmark multiple GNN layers and identify an ARMAConv-based architecture that offers a strong accuracy-runtime trade-off. Across horizons of 30-90 day simulation and prediction, allowing up to three contact change points, the surrogate model attains 10-27 \% mean absolute percentage error (MAPE) while delivering (near) constant runtime with respect to the forecast horizon. Our approach accelerates evaluation by up to 28,670 times compared with the mechanistic model, allowing responsive decision support in time-critical scenarios and straightforward web integration. These results show how GNN surrogates can translate complex metapopulation models into immediate, reliable tools for pandemic response.

翻译：在COVID-19危机期间，机制模型为循证决策提供了指导。然而，动态环境中的时效性决策限制了收集支持证据的时间。我们通过开发图神经网络（GNN）代理模型来解决这一瓶颈，该模型替代了具有年龄结构和空间分辨率的机制性元种群仿真模型。这种组合方法既补充了以机制为主的经典建模方法，也弥补了纯数据驱动的机器学习方法常被视为黑箱的不足。我们的实验设计涵盖了疫情暴发期与持续威胁期两种状态，最多包含三个接触变化节点，并在具有400个节点（代表德国县级行政区）的空间图上采用年龄结构接触矩阵。我们对多种GNN层进行基准测试，确定了基于ARMAConv的架构在精度与运行时间之间实现了较优平衡。在30-90天的仿真与预测范围内，允许最多三个接触变化节点的情况下，代理模型实现了10-27%的平均绝对百分比误差（MAPE），同时其运行时间相对于预测范围保持（近似）恒定。相较于机制模型，我们的方法将评估速度提升了最高28,670倍，从而能够在时效性场景中提供快速决策支持，并易于实现网络集成。这些结果表明，GNN代理模型能够将复杂的元种群模型转化为即时、可靠的疫情应对工具。