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arXiv · 2507.18269

Designing efficient interventions for pre-disease states using control theory

Makito Oku
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Abstract

To extend healthy life expectancy in an aging society, it is crucial to prevent various diseases at pre-disease states. Although dynamical network biomarker theory has been developed for pre-disease detection, mathematical frameworks for pre-disease treatment have not been well established. Here I propose a control theory-based approach for pre-disease treatment, named Markov chain sparse control (MCSC), where time evolution of a probability distribution on a Markov chain is described as a discrete-time linear system. By designing a sparse controller, a few candidate states for intervention are identified. The validity of MCSC is demonstrated using numerical simulations and real-data analysis.

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Makito Oku. 2025-07-24. Designing efficient interventions for pre-disease states using control theory. https://doi.org/10.1587/nolta.17.156

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