Fits time series models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including measures of health inequality, period and cumulative percent change, and age-standardized rates. Calculates Theil's index to measure inequality among multiple groups, and can be extended to measure inequality across multiple groups nested within geographies. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. The models are appropriate for disease incidence and mortality data, employing a Poisson or binomial likelihood and first-difference (random-walk) prior for unknown risk, and optional covariance matrix for multiple correlated time series models.
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