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E-raamat: Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny

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"This book shows how to model disease risk and quantify risk factors using areal and geostatistical data. It also shows how to create interactive maps of disease risk and risk factors, and describes how to build interactive dashboards and Shiny web applications that facilitate the communication of insights to collaborators and policy makers"--

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.

Arvustused

"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

Preface xiii
About the author xix
I Geospatial health data and INLA
1(50)
1 Geospatial health
3(4)
1.1 Geospatial health data
3(1)
1.2 Disease mapping
4(1)
1.3 Communication of results
5(2)
2 Spatial data and R packages for mapping
7(20)
2.1 Types of spatial data
7(3)
2.1.1 Areal data
7(2)
2.1.2 Geostatistical data
9(1)
2.1.3 Point patterns
9(1)
2.2 Coordinate reference systems
10(5)
2.2.1 Geographic coordinate systems
11(1)
2.2.2 Projected coordinate systems
12(1)
2.2.3 Setting Coordinate Reference Systems in R
13(2)
2.3 Shapefiles
15(3)
2.4 Making maps with R
18(9)
2.4.1 ggplot2
19(2)
2.4.2 leaflet
21(1)
2.4.3 mapview
22(3)
2.4.4 tmap
25(2)
3 Bayesian inference and INLA
27(6)
3.1 Bayesian inference
27(2)
3.2 Integrated nested Laplace approximation
29(4)
4 The R-INLA package
33(18)
4.1 Linear predictor
34(1)
4.2 The inla() function
34(1)
4.3 Priors specification
35(2)
4.4 Example
37(12)
4.4.1 Data
37(1)
4.4.2 Model
38(1)
4.4.3 Results
39(10)
4.5 Control variables to compute approximations
49(2)
II Modeling and visualization
51(124)
5 Areal data
53(22)
5.1 Spatial neighborhood matrices
54(3)
5.2 Standardized incidence ratio
57(5)
5.3 Spatial small area disease risk estimation
62(9)
5.3.1 Spatial modeling of lung cancer in Pennsylvania
64(7)
5.4 Spatio-temporal small area disease risk estimation
71(3)
5.5 Issues with areal data
74(1)
6 Spatial modeling of areal data. Lip cancer in Scotland
75(18)
6.1 Data and map
75(3)
6.2 Data preparation
78(2)
6.2.1 Adding data to map
79(1)
6.3 Mapping SIRs
80(3)
6.4 Modeling
83(4)
6.4.1 Model
83(1)
6.4.2 Neighborhood matrix
83(1)
6.4.3 Inference using INLA
84(1)
6.4.4 Results
85(2)
6.5 Mapping relative risks
87(1)
6.6 Exceedance probabilities
88(5)
7 Spatio-temporal modeling of areal data. Lung cancer in Ohio
93(18)
7.1 Data and map
93(2)
7.2 Data preparation
95(6)
7.2.1 Observed cases
95(1)
7.2.2 Expected cases
96(2)
7.2.3 SIRs
98(1)
7.2.4 Adding data to map
98(3)
7.3 Mapping SIRs
101(1)
7.4 Time plots of SIRs
102(4)
7.5 Modeling
106(2)
7.5.1 Model
106(1)
7.5.2 Neighborhood matrix
106(1)
7.5.3 Inference using INLA
107(1)
7.6 Mapping relative risks
108(3)
8 Geostatistical data
111(22)
8.1 Gaussian random fields
111(4)
8.2 Stochastic partial differential equation approach
115(1)
8.3 Spatial modeling of rainfall in Parana, Brazil
116(13)
8.3.1 Model
116(1)
8.3.2 Mesh construction
117(2)
8.3.3 Building the SPDE model on the mesh
119(1)
8.3.4 Index set
120(1)
8.3.5 Projection matrix
120(2)
8.3.6 Prediction data
122(2)
8.3.7 Stack with data for estimation and prediction
124(1)
8.3.8 Model formula
125(1)
8.3.9 inlaO call
125(1)
8.3.10 Results
125(1)
8.3.11 Projecting the spatial field
126(3)
8.4 Disease mapping with geostatistical data
129(4)
9 Spatial modeling of geostatistical data. Malaria in The Gambia
133(22)
9.1 Data
133(1)
9.2 Data preparation
134(6)
9.2.1 Prevalence
134(2)
9.2.2 Transforming coordinates
136(1)
9.2.3 Mapping prevalence
137(1)
9.2.4 Environmental covariates
137(3)
9.3 Modeling
140(5)
9.3.1 Model
140(1)
9.3.2 Mesh construction
141(1)
9.3.3 Building the SPDE model on the mesh
141(1)
9.3.4 Index set
142(1)
9.3.5 Projection matrix
142(1)
9.3.6 Prediction data
143(1)
9.3.7 Stack with data for estimation and prediction
144(1)
9.3.8 Model formula
145(1)
9.3.9 inla() call
145(1)
9.4 Mapping malaria prevalence
145(5)
9.5 Mapping exceedance probabilities
150(5)
10 Spatio-temporal modeling of geostatistical data. Air pollution in Spain
155(20)
10.1 Map
155(3)
10.2 Data
158(4)
10.3 Modeling
162(10)
10.3.1 Model
163(1)
10.3.2 Mesh construction
163(1)
10.3.3 Building the SPDE model on the mesh
164(1)
10.3.4 Index set
165(1)
10.3.5 Projection matrix
166(1)
10.3.6 Prediction data
166(2)
10.3.7 Stack with data for estimation and prediction
168(1)
10.3.8 Model formula
169(1)
10.3.9 inla() call
170(1)
10.3.10 Results
170(2)
10.4 Mapping air pollution predictions
172(3)
III Communication of results
175(86)
11 Introduction to R Markdown
177(12)
11.1 R Markdown
177(1)
11.2 YAML
178(1)
11.3 Markdown syntax
179(1)
11.4 R code chunks
180(2)
11.5 Figures
182(2)
11.6 Tables
184(1)
11.7 Example
184(5)
12 Building a dashboard to visualize spatial data with flexdash-board
189(14)
12.1 The R package flexdashboard
189(3)
12.1.1 R Markdown
190(1)
12.1.2 Layout
190(1)
12.1.3 Dashboard components
191(1)
12.2 A dashboard to visualize global air pollution
192(11)
12.2.1 Data
192(2)
12.2.2 Table using DT
194(1)
12.2.3 Map using leaflet
195(2)
12.2.4 Histogram using ggplot2
197(1)
12.2.5 R Markdown structure. YAML header and layout
197(2)
12.2.6 R code to obtain the data and create the visualizations
199(4)
13 Introduction to Shiny
203(14)
13.1 Examples of Shiny apps
203(2)
13.2 Structure of a Shiny app
205(1)
13.3 Inputs
206(1)
13.4 Outputs
207(1)
13.5 Inputs, outputs and reactivity
208(1)
13.6 Examples of Shiny apps
209(3)
13.6.1 Example 1
209(2)
13.6.2 Example 2
211(1)
13.7 HTML content
212(1)
13.8 Layouts
213(2)
13.9 Sharing Shiny apps
215(2)
14 Interactive dashboards with flexdashboard and Shiny
217(8)
14.1 An interactive dashboard to visualize global air pollution
218(7)
15 Building a Shiny app to upload and visualize spatio-temporal data
225(30)
15.1 Shiny
225(2)
15.2 Setup
227(1)
15.3 Structure of app.R
227(1)
15.4 Layout
228(1)
15.5 HTML content
229(2)
15.6 Read data
231(1)
15.7 Adding outputs
231(6)
15.7.1 Table using DT
231(1)
15.7.2 Time plot using dygraphs
232(2)
15.7.3 Map using leaflet
234(3)
15.8 Adding reactivity
237(8)
15.8.1 Reactivity in dygraphs
239(1)
15.8.2 Reactivity in leaflet
240(5)
15.9 Uploading data
245(3)
15.9.1 Inputs in ui to upload a CSV file and a shapefile
245(1)
15.9.2 Uploading CSV file in server()
246(1)
15.9.3 Uploading shapefile in server()
246(1)
15.9.4 Accessing the data and the map
247(1)
15.10 Handling missing inputs
248(2)
15.10.1 Requiring input files to be available using req()
248(1)
15.10.2 Checking data are uploaded before creating the map
249(1)
15.11 Conclusion
250(5)
16 Disease surveillance with SpatialEpiApp
255(6)
16.1 Installation
255(1)
16.2 Use of SpatialEpiApp
256(5)
16.2.1 `Inputs' page
256(1)
16.2.2 `Analysis' page
256(4)
16.2.3 `Help' page
260(1)
Appendix
261(4)
A R installation and packages used in the book
261(4)
A.1 Installing R and RStudio
261(1)
A.2 Installing R packages
262(1)
A.3 Packages used in the book
262(3)
Bibliography 265(8)
Index 273
Paula Moraga is a Lecturer in the Department of Mathematical Sciences at the University of Bath. She received her Masters 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.
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