Reconstructing model humans from observed health data

Human aging can be understood as a stochastic process of damage accumulation. This stochasticity is evident in the heterogeneity of health trajectories and lifespans of individuals. Measurements of health “deficits” can be used to quantify an individual's state of health. We model human aging using a network of interacting health deficits with stochastic damage/repair. Our model incorporates both “observed” nodes, corresponding to observed deficits, and “hidden” nodes, representing the large amount of health aspects that are not measured. We use maximum likelihood techniques to estimate parameters with observed human data of deficits and death ages. We then generate individuals from our model using these estimated parameters, so that they have health trajectories distributed approximately the same as the data. This lets us extrapolate from observed data to future health trajectories and lifespans.