Public health 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 health inequalities, cumulative and period percent change, and age-standardized rates.

surveil is an interface to Stan, a state-of-the-art platform for Bayesian inference.

Installation

surveil is available from CRAN; install from R using:

install.packages("surveil")

Usage

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)

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

Cumulative percent change in US cancer incidence by age group