In ConnorDonegan/surveil: Time Series Models for Disease Surveillance

surveil: 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 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.

Installation

surveil is available on CRAN; install from R using:

install.packages("surveil")

Vignettes

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.

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")

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

ConnorDonegan/surveil documentation built on July 12, 2024, 9:38 a.m.