Generating individual aging trajectories with a network model

Aging is characterized by the stochastic accumulation of damage. We model this process with a set of health attributes as nodes that interact in a network leading to mortality. Health decline and mortality occur stochastically with a set of complex rates that are trained along with the interaction network using data from observational aging studies. Our model generates synthetic-individuals with health trajectories and lifespan resembling real individuals. The trajectories and lifespans generated with this method also let us model both health events and health interventions so as to better understand their effects on the future health and lifespan of individuals.