  The vision of personalized medicine is to identify interventions that
maintain or restore a person's health based on their individual biology.
Medical digital twins, computational models that integrate a wide range of
health-related data about a person and can be dynamically updated, are a key
technology that can help guide medical decisions. Such medical digital twin
models can be high-dimensional, multi-scale, and stochastic. To be practical
for healthcare applications, they often need to be simplified into
low-dimensional surrogate models that can be used for the optimal design of
interventions. This paper introduces surrogate modeling algorithms for the
purpose of optimal control applications. As a use case, we focus on agent-based
models (ABMs), a common model type in biomedicine for which there are no
readily available optimal control algorithms. By deriving surrogate models that
are based on systems of ordinary differential equations, we show how optimal
control methods can be employed to compute effective interventions, which can
then be lifted back to a given ABM. The relevance of the methods introduced
here extends beyond medical digital twins to other complex dynamical systems.
