Disease risk modelling and visualization using R Paula Moraga - - PowerPoint PPT Presentation

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Disease risk modelling and visualization using R Paula Moraga - - PowerPoint PPT Presentation

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Disease risk modelling and visualization using R Paula Moraga useR! 2018 Brisbane, 10 July 2018 1/51 Outline Introduction to disease mapping Tutorials Tutorial: areal data Tutorial: geostatistical data Presentations options: interactive

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Disease risk modelling and visualization using R

Paula Moraga

useR! 2018 Brisbane, 10 July 2018

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Outline

Introduction to disease mapping Tutorials Tutorial: areal data Tutorial: geostatistical data Presentations options: interactive dashboards and Shiny apps SpatialEpiApp Shiny Tutorial: Shiny

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Introduction to disease mapping

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John Snow’s map of cholera deaths in Soho, London, 1854

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Disease mapping

Disease maps help understand the spatial patterns of disease and its

  • determinants. This information can guide decision makers and

programme managers to better allocate limited resources and to design strategies for disease prevention and control

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Types of spatial data

  • 1. Areal data
  • 2. Geostatistical data
  • 3. Point patterns

Moraga and Lawson 2012 Moraga et al. 2015 Moraga and Montes 2011 6/51

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Modelling

  • Disease risk predictions are based on the observed disease

cases, the number of individuals at risk, and risk factors information such as demographic and environmental factors

  • Models describe the variability in the response variable as a

function of the risk factors covariates and random effects to account for unexplained variability

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Areal data

Moraga and Lawson 2012 8/51

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Areal data

Disease risk is often estimated by the Standardized Mortality Ratio: SMRi = Yi Ei

  • Yi number of observed cases in area i
  • Ei number of expected cases in area i (number expected cases

if area had the same disease rate as the standard population)

  • SMRi > 1 (< 1): more (fewer) cases observed than expected
  • Expected cases calculated using indirect standardization

Ei =

m

  • j=1

r(s)

j n(i) j

  • r(s)

j

disease rate in strata j in standard population = (number disease cases)/(population) in strata j in standard pop

  • n(i)

j

population in strata j in area i

  • strata j can indicate age group, sex, etc

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Areal data

  • SMRs may be misleading and insufficiently reliable in areas

with small populations

  • In contrast, model-based approaches enable to incorporate

covariates and borrow information from neighboring areas to improve local estimates, resulting in the smoothing of extreme rates based on small sample sizes

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Areal data

Model to estimate disease risks θi in areas i = 1, . . . , n Yi|θi ∼ Po(Ei × θi), log(θi) = z′

iβ + ui + vi

  • ui is an structured spatial effect to account for the spatial

dependence between relative risks (areas that are close show more similar risk than areas that are not close)

  • vi is an unstructured spatial effect to account for independent

area-specific noise

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Geostatistical data

Moraga et al. 2015 12/51

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Geostatistical data

Yi|P(xi) ∼ Binomial(Ni, P(xi)), logit(P(xi)) = z′

iβ + S(xi) + vi

Risk factors covariates

(e.g. temperature, precipitation, vegetation, etc)

NASA Earth Observations

Gaussian Random Field

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Coordinate Reference Systems (CRS)

1 unprojected or geographic: Latitude/Longitude for

referencing location on the ellipsoid Earth

2 projected: Easting/Northing for referencing location on

2-dimensional representation of Earth. Common projection: Universal Transverse Mercator (UTM)

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Tutorials

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Install R packages

install.packages(c("dplyr", "ggplot2", "leaflet", "geoR", "rgdal", "raster", "sp", "spdep", "SpatialEpi", "SpatialEpiApp")) install.packages("INLA", repos = "https://inla.r-inla-download.org/R/stable", dep = TRUE)

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Tutorial: areal data

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Areal data. Lung cancer in Pennsylvania

https://paula-moraga.github.io/tutorial-areal-data/

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Tutorial: geostatistical data

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Geostatistical data. Malaria in The Gambia

https://paula-moraga.github.io/tutorial-geostatistical-data/

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Presentations options: interactive dashboards and Shiny apps

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Interactive dashboards with flexdashboard

  • https://rmarkdown.rstudio.com/flexdashboard/
  • Uses R Markdown to publish a group of related data

visualizations as a dashboard

  • Components that can be included include plots, tables, value

boxes and htmlwidgets

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Layout

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Example

https://rmarkdown.rstudio.com/flexdashboard/examples.html

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Interactive Shiny web applications

  • https://shiny.rstudio.com/
  • Shiny is a web application framework for R that enables to

build interactive web applications

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SpatialEpiApp

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R package SpatialEpiApp

  • Shiny web application that allows to visualize spatial and

spatio-temporal disease data, estimate disease risk and detect clusters

  • Risk estimates by fitting Bayesian models with INLA
  • Detection of clusters by using the scan statistics in SaTScan

Launch SpatialEpiApp: install.packages("SpatialEpiApp") library(SpatialEpiApp) run_app()

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Data entry

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Interactive

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Maps

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Clusters

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Report

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Shiny

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Shiny

  • Shiny is a web application framework for R that enables to

build interactive web applications

  • https://shiny.rstudio.com/

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Examples

https://shiny.rstudio.com/gallery/single-file-shiny-app.html

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Examples

https://shiny.rstudio.com/gallery/telephones-by-region.html

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Structure of a Shiny App

A Shiny app is a directory that contains an R file called app.R. app.R has three components:

  • user interface object (ui) which controls the layout and

appearance of the app

  • server() function with the instructions to build the objects

displayed in the ui

  • call to shinyApp() that creates the Shiny app from the

ui/server pair

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Content app.R

# define user interface object ui <- fluidPage( ) # define server() function server <- function(input, output){ } # call to shinyApp() which returns the Shiny app shinyApp(ui = ui, server = server) Save app.R inside the appdir directory. Launch the app: library(shiny) runApp("appdir_path")

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Inputs

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Outputs

  • Plots, tables, texts, images

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Inputs, outputs and reactivity

  • Inputs: we can interact with the app by modifying their values
  • Outputs: objects we want to show in the app

ui <- fluidPage( *Input(inputId = myinput, label = mylabel, ...) *Output(outputId = myoutput, ...) ) server <- function(input, output){

  • utput$myoutput <- render*({

# code to build the output. # If it uses an input value (input$myinput), # the output will be rebuilt whenever # the input value changes })}

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Inputs, outputs and reactivity

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HTML widgets

Interactive web visualizations using JavaScript http://www.htmlwidgets.org/

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Leaflet

http://rstudio.github.io/leaflet/

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Dygraphs

http://rstudio.github.io/dygraphs

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DataTables

http://rstudio.github.io/DT/

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Options to share a Shiny app

1 Share R scripts with other users

  • need R

library(shiny) runApp("appdir_path")

2 Host app as a web page at its own URL

  • do not need R
  • app can be navigated through the internet with a web browser
  • host apps on own servers or using one of the ways RStudio
  • ffers such as shinyapps.io and Shiny Server

https://paulamoraga.shinyapps.io/spatialepiapp/

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Tutorial: Shiny

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Tutorial: Shiny

https://paula-moraga.github.io/tutorial-shiny-spatial/

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References

  • Paula Moraga. SpatialEpiApp: A Shiny Web Application for

the analysis of Spatial and Spatio-Temporal Disease Data, (2017), Spatial and Spatio-temporal Epidemiology, 23:47-57

  • Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan

McPherson (2017). shiny: Web Application Framework for R. https://CRAN.R-project.org/package=shiny

  • Barbara Borges and JJ Allaire (2017). flexdashboard: R

Markdown Format for Flexible Dashboards. https://CRAN.R-project.org/package=flexdashboard

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Thanks!

https://Paula-Moraga.github.io Twitter @_PaulaMoraga_

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