{"ID":2890849,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.18269","arxiv_id":"2507.18269","title":"Designing efficient interventions for pre-disease states using control theory","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.","short_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 the...","url_abs":"https://arxiv.org/abs/2507.18269","url_pdf":"https://arxiv.org/pdf/2507.18269v2","authors":"[\"Makito Oku\"]","published":"2025-07-24T10:12:01Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}