Knapp, A. C.; Cruz, D. A.; Mehrad, B.; Laubenbacher, R. C.

A medical digital twin is a computational replica of some aspect of a patient\'s biology relevant to patient health. It consists of a computational model that is calibrated to the patient and is dynamically updated using a stream of patient data. The underlying computational model is often complex, multi-state, stochastic, and combines different modeling platforms at different scales. Standard methods for data assimilation do not directly apply to such models. This is true in particular for ensemble Kalman filter methods, a common approach to such problems. This paper focuses on agent-based models (ABMs), a model type often used in biomedicine. The key challenge for any forecasting algorithm for this model type is to bridge the gap between (detailed) micro- and (summary) macrostates. This paper proposes a modified Kalman filter method to meet this challenge, providing a way to dynamically update the microstate of an ABM using patient measurements collected at the macro level.