This paper presents a hybrid modeling approach that couples an Agent-Based Model (ABM) with a partial differential equation (PDE) model in an epidemic setting to simulate the spatial spread of infectious diseases using a compartmental structure with seven health states. The goal is to reduce the computational complexity of a full-ABM by introducing a coupled ABM-PDE model that offers significantly faster simulations while maintaining comparable accuracy. Our results demonstrate that the hybrid model not only reduces the overall simulation runtime (defined as the number of runs required for stable results multiplied by the duration of a single run) but also achieves smaller errors across both 25% and 100% population samples. The coupling mechanism ensures consistency at the model interface: agents crossing from the ABM into the PDE domain are removed and represented as density contributions, while surplus density in the PDE domain is used to generate agents with plausible trajectories derived from mobile phone data. We evaluate the hybrid model using real-world mobility and infection data for the Berlin-Brandenburg region in Germany, showing that it captures the core epidemiological dynamics while enabling efficient large-scale simulations.

翻译：本文提出了一种混合建模方法，在流行病学背景下将基于智能体的模型（ABM）与偏微分方程（PDE）模型耦合，利用包含七种健康状态的仓室结构来模拟传染病的空间传播。其目标是通过引入耦合的ABM-PDE模型，在保持相当精度的同时显著加快模拟速度，从而降低完全ABM模型的计算复杂度。我们的结果表明，该混合模型不仅减少了总体模拟运行时间（定义为获得稳定结果所需的运行次数乘以单次运行的持续时间），而且在25%和100%人口样本上都实现了更小的误差。耦合机制确保了模型接口的一致性：从ABM域进入PDE域的智能体被移除并表示为密度贡献，而PDE域中的过剩密度则用于生成具有从手机数据推导出的合理轨迹的智能体。我们使用德国柏林-勃兰登堡地区的真实世界流动性和感染数据对该混合模型进行了评估，结果表明它能够捕捉核心流行病学动态，同时实现高效的大规模模拟。