Geospatial Health Data : Modeling and Visualization with R-INLA and Shiny book cover
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Geospatial Health Data
Modeling and Visualization with R-INLA and Shiny




ISBN 9780367357955
Published November 25, 2019 by Chapman and Hall/CRC
294 Pages

 
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Book 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:

  • Manipulating and transforming point, areal, and raster data,
  • Bayesian hierarchical models for disease mapping using areal and geostatistical data,
  • Fitting and interpreting spatial and spatio-temporal models with the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches,
  • Creating 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 policymakers.

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, modelling, 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. 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

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Author(s)

Biography

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.

Reviews

"The stress is on practical usage of INLA modelling in a spatial context and hence the author shows the full code for several carefully selected examples. Essentially all the steps from the beginning (necessary data manipulation and preparation) via INLA analysis itself (often in several alternatives) to the results (plots and maps) are explained carefully and commented. This is very useful for anybody who wants to start with the powerful INLA but did not dare to go through the very powerful but notalways- fully-documented environment." ~Marek Brabec, ISCB News