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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("demonstration"): An introduction to health
modeling with surveilvignette("age-standardization"): How to age-standardize
surveil model results and compare age-stratified
populationsAlso 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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.