Inference of latent epidemic regimes and generative simulations reveal how inequality and mobility shape COVID-19 transmission.

Epidemic waves in large metropolitan areas unfold heterogeneously across territories shaped by persistent socioeconomic inequalities. Explaining how transmission intensifies, stabilises, and shifts across the urban landscape remains a central challenge in epidemiology. This study develops a covariate-dependent, non-homogeneous Hidden Markov Model (nHMM) to infer latent transmission regimes from municipality-level COVID-19 incidence in Santiago, Chile. The framework links daily case dynamics to mobility flows and structural socioeconomic indicators within a hierarchical specification that captures inter-municipal heterogeneity. Model selection identifies three statistically distinct and epidemiologically interpretable regimes corresponding to moderate, severe, and critical transmission phases. Transition dynamics reveal marked spatial asymmetries: while increases in mobility consistently elevate escalation risk, structural conditions-such as overcrowding and deficits in urban infrastructure-substantially influence both the probability of entering and the persistence within high-severity regimes. To ensure epidemiological interpretability, regime-conditioned incidence trajectories are mapped to the time-varying reproduction number ([Formula: see text]) through a renewal formulation, enabling coherent propagation of uncertainty from latent-state inference to transmission estimates. By integrating latent regime inference with hierarchical transition modelling and renewal-based transmission analysis, this study distinguishes structural phase shifts from stochastic variability in urban epidemic dynamics. The findings clarify how mobility and entrenched inequality jointly structure transmission risk, providing a scalable and transferable framework for monitoring and anticipating epidemic regime transitions in complex metropolitan systems.