Geospatial Health Data-Modeling and Visualization with R-INLA and Shiny

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