A Hybrid ABM-PDE Framework for Real-World Infectious Disease Simulations

2025-04-11

AI Summary

This paper addresses the challenge of balancing accuracy and computational efficiency in large-scale spatial epidemic simulation. We propose a hybrid agent-based model–partial differential equation (ABM–PDE) framework. Methodologically, we design a seven-compartment epidemiological model and introduce, for the first time, a dynamic bidirectional coupling mechanism: agents transitioning from ABM to PDE domains are converted into localized density sources; conversely, when PDE-predicted population densities exceed a threshold, new agents are instantiated using real-world mobile phone trajectory data—ensuring self-consistent integration of micro-level behavioral dynamics and macro-level continuum fields. Experiments on Berlin–Brandenburg regional data demonstrate that our approach reduces computation time by over 60% compared to pure ABM, while significantly lowering simulation error under both 25% and 100% population sampling. Moreover, it accurately reproduces key spatiotemporal transmission dynamics of regional outbreaks.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Security and Privacy: Large-scale security measurements

Abstract

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 at the corresponding grid node, 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.

Problem

• Reducing computational complexity in disease spread simulations
• Coupling ABM-PDE for faster, accurate infectious disease modeling
• Validating hybrid model with real-world mobility and infection data

Innovation

• Hybrid ABM-PDE model for disease simulation
• Coupling reduces complexity, maintains accuracy
• Uses mobile data for agent trajectories