The R Journal: accepted article

This article will be copy edited and may be changed before publication.

Small Area Disease Risk Estimation and Visualization Using R PDF download
Paula Moraga

Abstract Small area disease risk estimation is essential for disease prevention and control. In this paper, we demonstrate how R can be used to obtain disease risk estimates and quantify risk factors using areal data. We explain how to define disease risk models and how to perform Bayesian inference using the INLA package. We also show how to make interactive maps of estimates using the leaflet package to better understand the disease spatial patterns and communicate the results. We show an example of lung cancer risk in Pennsylvania, United States, in year 2002, and demonstrate that R represents an excellent tool for disease surveillance by enabling reproducible health data analysis.

Received: 2018-02-03; online 2018-06-07, supplementary material, (1.7 Kb)
CRAN packages: leaflet, SpatialEpi, spdep, ggplot2, flexdashboard, shiny, SpatialEpiApp, dygraphs, DT, rmarkdown
CRAN Task Views implied by cited CRAN packages: ReproducibleResearch, Spatial, Econometrics, Graphics, Phylogenetics, TimeSeries, WebTechnologies


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-036,
  author = {Paula Moraga},
  title = {{Small Area Disease Risk Estimation and Visualization Using R}},
  year = {2018},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-036/index.html}
}