The Coupled Stochastic Dynamical System: A Generative Model for Simulating and Forecasting Youth Mental Health Trajectories

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Abstract
Predicting individual mental-health trajectories requires models that capture cross-domain dynamics rather than only statistical associations. We introduce the Coupled Stochastic Dynamical System (CSDS), a forward generative model of interacting digital behaviour, wearable-derived physiology and psychiatric burden. In the multi-year GLOBEM student cohort (N=496), Bayesian inversion recovered four behavioural phenotypes, reproducing 80–83% of cluster targets through distinct trait-vulnerability, physical-activity and affective-response parameter combinations. These phenotypes were primarily differentiated by digital-media overuse, physical activity, sleep and affective burden. Participant-specific inversion then generated virtual twins whose synthetic trajectories preserved observed behavioural traces: hidden Markov models trained on virtual-twin data reproduced state occupancy and transition patterns ( r =0.84–0.87 and 0.93–0.94). Forecasts outperformed causal mean and persistence baselines for short-horizon magnitude prediction and predicted affective-state changes with 66–78% balanced accuracy over 21 days. These findings establish CSDS as an interpretable framework for simulating individual behavioural dynamics and advancing personalised mental-health modelling.

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