Designing efficient interventions for pre-disease states using control theory

TL;DR

This paper introduces a control theory-based framework called Markov chain sparse control (MCSC) for designing targeted interventions in pre-disease states, aiming to prevent disease progression in aging populations.

Contribution

It develops a novel mathematical approach using control theory and Markov chains for pre-disease treatment, filling a gap in existing dynamical network biomarker methods.

Findings

MCSC effectively identifies key intervention states.

Numerical simulations validate the approach.

Real-data analysis supports its practical applicability.

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.