Disease processes as hybrid dynamical systems

TL;DR

This paper introduces a hybrid dynamical systems framework for modeling complex infectious diseases, integrating multiscale biological interactions with control theory to optimize treatment scheduling.

Contribution

It develops an algebraic language for disease processes and treatments, enabling hybrid models that combine continuous and discrete dynamics for improved disease management.

Findings

Hybrid models effectively describe multiscale disease processes.

Control-theoretic methods can optimize therapy schedules.

Application to SIR model demonstrates potential benefits.

Abstract

We investigate the use of hybrid techniques in complex processes of infectious diseases. Since predictive disease models in biomedicine require a multiscale approach for understanding the molecule-cell-tissue-organ-body interactions, heterogeneous methodologies are often employed for describing the different biological scales. Hybrid models provide effective means for complex disease modelling where the action and dosage of a drug or a therapy could be meaningfully investigated: the infection dynamics can be classically described in a continuous fashion, while the scheduling of multiple treatment discretely. We define an algebraic language for specifying general disease processes and multiple treatments, from which a semantics in terms of hybrid dynamical system can be derived. Then, the application of control-theoretic tools is proposed in order to compute the optimal scheduling of multiple therapies. The potentialities of our approach are shown in the case study of the SIR epidemic model and we discuss its applicability on osteomyelitis, a bacterial infection affecting the bone remodelling system in a specific and multiscale manner. We report that formal languages are helpful in giving a general homogeneous formulation for the different scales involved in a multiscale disease process; and that the combination of hybrid modelling and control theory provides solid grounds for computational medicine.