Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny, 1st Edition (Hardback) book cover

Geospatial Health Data

Modeling and Visualization with R-INLA and Shiny, 1st Edition

By Paula Moraga

Chapman and Hall/CRC

282 pages

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Hardback: 9780367357955
pub: 2019-11-20
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Description

Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics:

  • Manipulate and transform point, areal, and raster data,
  • Bayesian hierarchical models for disease mapping using areal and geostatistical data,
  • Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches,
  • Create interactive and static visualizations such as disease maps and time plots,
  • Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers.

The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.

Table of Contents

I Geospatial health data and INLA 1

1. Geospatial health

Geospatial health data

Disease mapping

Communication of results

2. Spatial data and R packages for mapping

Types of spatial data

Areal data

Geostatistical data

Point patterns

Coordinate Reference Systems (CRS)

Geographic coordinate systems

Projected coordinate systems

Setting Coordinate Reference Systems in R

Shapefiles

Making maps with R

ggplot2

leaflet

mapview

tmap

3. Bayesian inference and INLA

Bayesian inference

Integrated Nested Laplace Approximations (INLA)

4. The R-INLA package

Linear predictor

The inla() function

Priors specification

Example

Data

Model

Results

Control variables to compute approximations

II Modeling and visualization

5. Areal data

Spatial neighborhood matrices

Standardized Incidence Ratio (SIR)

Spatial small area disease risk estimation

Spatial modeling of lung cancer in Pennsylvania

Spatio-temporal small area disease risk estimation

Issues with areal data

6. Spatial modeling of areal data. Lip cancer in Scotland

Data and map

Data preparation

Adding data to map

Mapping SIRs

Modeling

Model

Neighborhood matrix

Inference using INLA

Results

Mapping relative risks

Exceedance probabilities

7. Spatio-temporal modeling of areal data. Lung cancer in Ohio

Data and map

Data preparation

Observed cases

Expected cases

SIRs

Adding data to map

Mapping SIRs

Time plots of SIRs

Modeling

Model

Neighborhood matrix

Inference using INLA

Mapping relative risks

8. Geostatistical data

Gaussian random fields

Stochastic Partial Differential Equation approach (SPDE)

Spatial modeling of rainfall in Paraná, Brazil

Model

Mesh construction

Building the SPDE model on the mesh

Index set

Projection matrix

Prediction data

Stack with data for estimation and prediction

Model formula

inla() call

Results

Projecting the spatial field

Disease mapping with geostatistical data

9. Spatial modeling of geostatistical data. Malaria in The Gambia

Data

Data preparation

Prevalence

Transforming coordinates

Mapping prevalence

Environmental covariates

Modeling

Model

Mesh construction

Building the SPDE model on the mesh

Index set

Projection matrix

Prediction data

Stack with data for estimation and prediction

Model formula

inla() call

Mapping malaria prevalence

Mapping exceedance probabilities

10. Spatio-temporal modeling of geostatistical data. Air pollution in Spain

Map

Data

Modeling

Model

Mesh construction

Building the SPDE model on the mesh

Index set

Projection matrix

Prediction data

Stack with data for estimation and prediction

Model formula

inla() call

Results

Mapping air pollution predictions

III Communication of results

11. Introduction to R Markdown

R Markdown

YAML

Markdown syntax

R code chunks

Figures

Tables

Example

12. Building a dashboard to visualize spatial data with flexdashboard

The R package flexdashboard

R Markdown

Layout

Dashboard components

A dashboard to visualize global air pollution

Data

Table using DT

Map using leaflet

Histogram using ggplot2

R Markdown structure. YAML header and layout

R code to obtain the data and create the visualizations

13. Introduction to Shiny

Examples of Shiny apps

Structure of a Shiny app

Inputs

Outputs

Inputs, outputs and reactivity

Examples of Shiny apps

Example 1

Example 2

HTML Content

Layouts

Sharing Shiny apps

14. Interactive dashboards with flexdashboard and Shiny

An interactive dashboard to visualize global air pollution

15. Building a Shiny app to upload and visualize spatio-temporal data

Shiny

Setup

Structure of app.R

Layout

HTML content

Read data

Adding outputs

Table using DT

Time plot using dygraphs

Map using leaflet

Adding reactivity

Reactivity in dygraphs

Reactivity in leaflet

Uploading data

Inputs in ui to upload a CSV file and a shapefile

Uploading CSV file in server()

Uploading shapefile in server()

Accessing the data and the map

Handling missing inputs

Requiring input files to be available using req()

Checking data are uploaded before creating the map

Conclusion

16. Disease surveillance with SpatialEpiApp

Installation

Use of SpatialEpiApp

‘Inputs’ page

‘Analysis’ page

‘Help’ page

Appendix

A R installation and packages used in the book

A.1 Installing R and RStudio

A.2 Installing R packages

A.3 Packages used in the book

About the Author

Paula Moraga is a Lecturer in the Department of Mathematical Sciences at the University of Bath. She received her Master’s in Biostatistics from Harvard University and her Ph.D. in Statistics from the University of Valencia. Dr. Moraga develops innovative statistical methods and open-source software for disease surveillance including R packages for spatio-temporal modeling, detection of clusters, and travel-related spread of disease. Her work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries.

About the Series

Chapman & Hall/CRC Biostatistics Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
MED028000
MEDICAL / Epidemiology
MED037000
MEDICAL / Health Risk Assessment