  This study investigates the spatial integration of agent-based models (ABMs)
and compartmental models in infectious disease modeling, presenting a novel
hybrid approach and studying its implications. ABMs, characterized by
individual agent interactions and decision-making, offer detailed insights but
are computationally intensive for large populations. Compartmental models,
based on differential equations, provide population-level dynamics but lack
granular detail. Our hybrid model 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. We developed a custom ABM and a compartmental model, analyzing
their infectious disease dynamics separately before integrating them into a
hybrid model. This integration involved spatial coupling of discrete and
continuous populations and evaluating the consistency of disease dynamics at
the macro scale. Our key objectives were to assess the effect of model
hybridization on resulting infection dynamics, and to quantify computational
cost savings of the hybrid approach over the ABM. We show that the hybrid
approach can significantly reduce computational costs, but is sensitive to
between-model differences, highlighting that model equivalence is a crucial
component of hybrid modeling approaches. The code is available at
http://github.com/iebos/hybrid_model1.
