Time Series Models for Disease Surveillance

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 rare disease incidence and mortality data, employing a Poisson likelihood and first-difference (random-walk) prior for unknown risk, and optional covariance matrix for multiple correlated time series models. References: Brandt and Williams (2007, ISBN:978-1-4129-0656-2); Clayton (1996, ISBN-13:978-0-412-05551-5); Stan Development Team (2021) < https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).


Reference manual

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0.1.0 by Connor Donegan, 16 days ago

https://connordonegan.github.io/surveil/, https://github.com/ConnorDonegan/surveil/

Browse source code at https://github.com/cran/surveil

Authors: Connor Donegan [aut, cre]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports rstantools, methods, Rcpp, RcppParallel, rstan, tidybayes, dplyr, rlang, tidyr, ggplot2, gridExtra, scales, ggdist

Suggests rmarkdown, knitr, testthat

Linking to BH, Rcpp, RcppEigen, RcppParallel, rstan, StanHeaders

System requirements: GNU make

See at CRAN