README.md
In ConnorDonegan/surveil: Time Series Models for Disease Surveillance

The surveil R package provides time series models for routine public health surveillance tasks: model time trends in mortality or disease incidence rates to make inferences about levels of risk, cumulative and period percent change, age-standardized rates, and health inequalities.

surveil is an interface to Stan, a state-of-the-art platform for Bayesian inference. For analysis of spatial health data see the geostan R package.

surveil is available on CRAN; install from R using:

install.packages("surveil")

Review the package vignettes to get started:

vignette("surveil-demo"): An introduction to public health modeling with surveil
vignette("age-standardization"): How to age-standardize surveil model results
vignette("measuring-inequality"): Assessing pairwise health differences with measures of inequality
vignette("surveil-mcmc"): A brief introduction to Markov chain Monte Carlo (MCMC) with surveil

Also see the online documentation.

Model time series data of mortality or disease incidence by loading the surveil package into R together with disease surveillance data. Tables exported from CDC WONDER are automatically in the correct format.

library(surveil)
library(knitr)
data(cancer)

kable(head(cancer), 
      booktabs = TRUE,
      caption = "Table 1. A glimpse of cancer surveillance data")

| Year| Age | Count| Population| |-----:|:------|------:|-----------:| | 1999| \<1 | 866| 3708753| | 1999| 1-4 | 2959| 14991152| | 1999| 5-9 | 2226| 20146188| | 1999| 10-14 | 2447| 19742631| | 1999| 15-19 | 3875| 19585857| | 1999| 20-24 | 5969| 18148795|

Model trends in risk and easily view functions of risk estimates, such as cumulative percent change:

fit <- stan_rw(data = cancer,
               time = Year, 
               group = Age,
           cores = 4 # multi-core processing for speed
           )

fit_apc <- apc(fit)
plot(fit_apc, cumulative = TRUE)

Cumulative percent change in US cancer incidence by age group