Integrating Agent-Based and Compartmental Models for Infectious Disease Modeling: A Novel Hybrid Approach

TL;DR

This paper introduces a hybrid modeling approach combining agent-based and compartmental models to improve infectious disease simulation accuracy and efficiency, especially for large and diverse populations.

Contribution

It presents a novel spatially integrated hybrid model that balances detail and computational efficiency for infectious disease spread simulations.

Findings

Hybrid model reduces computational costs significantly.

Model sensitivity depends on between-model differences.

Effective for large, heterogeneous populations.

Abstract

This study investigates the spatial integration of agent-based models (ABMs) and compartmental models for infectious disease modeling, presenting a novel hybrid approach and examining its implications. ABMs offer detailed insights by simulating interactions and decisions among individuals but are computationally expensive for large populations. Compartmental models capture population-level dynamics more efficiently but lack granular detail. We developed a hybrid model that aims to balance the granularity of ABMs with the computational efficiency of compartmental models, offering a more nuanced understanding of disease spread in diverse scenarios, including large populations. This model spatially couples discrete and continuous populations by integrating an ordinary differential equation model with a spatially explicit ABM. Our key objectives were to systematically assess the consistency of disease dynamics and the computational efficiency across various configurations. For this, we evaluated two experimental scenarios and varied the influence of each sub-model via spatial distribution. In the first, the ABM component modeled a homogeneous population; in the second, it simulated a heterogeneous population with landscape-driven movement. Results show that the hybrid model can significantly reduce computational costs but is sensitive to between-model differences, highlighting the importance of model equivalence in hybrid approaches. The code is available at: git.zib.de/ibostanc/hybrid_abm_ode