Time Series Models for Disease Surveillance
Annual and cumulative percent change
Measures of pairwise inequality
Methods for fitted surveil models
Methods for APC objects
Prior distributions
Time series models for mortality and disease incidence
Methods for age-standardized rates
Age-standardized rates
The 'surveil' package
Methods for surveil_diff objects
Theil's inequality index
Methods for Theil's index
Widely Applicable Information Criteria
Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).