Mobility-Informed Coupling of ABM, PDE, and ODE Models for Pandemic Simulation in Germany
For policymakers and epidemiologists, this framework provides a scalable tool for evidence-based evaluation of public health interventions across interconnected regions.
The paper presents a hybrid modeling framework that couples agent-based, PDE, and ODE models with mobility data to simulate COVID-19 spread across Germany, enabling efficient and accurate evaluation of containment strategies like travel restrictions and Zero-COVID policies.
We present a hybrid modeling framework for simulating the spread of COVID-19 across Germany. Our approach couples high-resolution agent-based models (ABMs) incorporating mobility data from mobile phones with faster, less detailed partial differential equation (PDE) and ordinary differential equation (ODE) models. Mobility between regions is incorporated through data-driven jump processes that transfer individuals, enabling a balance between accuracy and computational efficiency. Building on earlier studies on pairwise ABM-ODE, ABM-PDE, and PDE-ODE coupling strategies, we develop a hybrid model to unify all three model classes (ABM, PDE, and ODE) within a single framework. To demonstrate the framework's utility, we systematically compare ABM, PDE, and ODE representations of Berlin embedded in a nationwide simulation of Germany, investigating complete travel restrictions to and from selected federal states, and evaluating the Zero-COVID and No-COVID strategies. These experiments demonstrate how the framework can be used to analyze the interplay between mobility, regional coupling, and containment measures at the scale of an entire country. Computational performance is analyzed by measuring runtime savings while quantifying error using real-world infection data. The presented framework enables efficient and accurate simulation of infection dynamics across densely connected regions and provides a tool for evidence-based evaluation of public health interventions.