Mobility-Informed Coupling of ABM, PDE, and ODE Models for Pandemic Simulation in Germany

Abstract

Simulating epidemic spread across an entire country requires balancing fine-grained realism with computational feasibility. We address this trade-off with a multiscale, hybrid modeling framework for simulating the spread of COVID-19 across Germany. The spatial domain is split into regions, each represented either by a high-resolution agent-based model (ABM) incorporating mobility data from mobile phones or by a faster, less detailed model based on partial (PDEs) or ordinary differential equations (ODEs). Data-driven jump processes model mobility between regions, enabling individuals to be transferred between model domains. Building on earlier studies on pairwise coupling strategies, we develop a unified framework that combines all three model classes within a single simulation environment. To demonstrate the framework's utility, we systematically compare ABM, PDE, and ODE representations of Berlin embedded in a nationwide simulation of Germany, investigate regional travel restrictions, and evaluate the Zero-COVID and No-COVID strategies. The results indicate that model resolution can be reduced in sufficiently homogeneous regions without substantially altering epidemic dynamics. Further, they reveal that mobility restrictions can lead to non-intuitive outcomes, including cases in which regional border closures increase infection numbers both locally and nationally. These effects are observed even between non-adjacent regions, illustrating how emergent, system-wide dynamics arise from local mobility restrictions. We quantify computational performance in terms of runtime savings and validate the framework against real-world infection data. The results show that the hybrid framework substantially reduces computational cost without sacrificing predictive accuracy, offering a practical tool for evaluating regional mobility restrictions and public health interventions at national scale.