Hybrid simulation is particularly useful in population health, since healthcare systems are characterized by both dynamic and stochastic complexity and the use of one single simulation approach may result in an oversimplified model that fails to address the real problem. This chapter presents the foundational concepts of hybrid simulation modeling and describes how the various stages in developing a single-method model can be adapted for hybrid simulation. These are illustrated by two examples from population health: age-related macular degeneration and dementia. In both cases, hybrid simulation has enabled the model to reflect the complexity of the decisions facing population health planners, who have to consider individual patient variability and the uncertainty of health outcomes from a “whole-system” perspective. The chapter presents a set of guidelines for modelers, showing how an integrated, multiscale simulation modeling framework can be developed, validated, and exploited for population health problems. The integration of micro-level modeling with macro-level modeling approaches, grounded in foundational complex systems properties and theories, can capture aspects of health systems that a single-method approach cannot.
Development of an industrial cyber-physical platform for small series production using digital twins
