inst/shiny/epinet/ui.R

##
## UI File for epinet Shiny Application
##
## Run local:
## Run online:
##

library(shiny)
library(EpiModel)

shinyUI(
navbarPage(title = NULL, windowTitle = "EpiModel: Network Models",
  tabPanel("About",

      column(6, offset = 1,
           h2("Stochastic Network Models with EpiModel", style = "color: #445555;"),
           p(a("EpiModel", href = "http://www.epimodel.org/", target = "_blank"),
             "is an R package that provides tools for simulating and
             analyzing mathematical models of infectious disease.
             Details about the package, including the epidemic model classes
             supported by the software can be found at the link above."),
           p("This web-based application allows for simple modeling of epidemics
              over dynamic contact networks. These stochastic network models are
             based on the statistical framework of", a("temporal exponential random
             graph models.", href = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3891677/",
             target = "_blank"), "This web application is built with",
             a("Shiny,", href = "http://shiny.rstudio.com/", target = "_blank"),
             "and may be lauched via an R session with EpiModel and Shiny installed
             (see the", code("epiweb"),"function), or directly on any web browser (no R
             needed)", a("here.", href = "https://statnet.shinyapps.io/epinet",
                         target = "_blank")),
           p("To get started, create a statistical network model in the Model Estimation
             page using one of the two model specification methods. This page fits
             a temporal ERGM using the", code("netest"), "function and runs diagnostics on the
             fitted model with the", code("netdx"), "function. After the model is properly
             specified, simulate an epidemic on the network using the Epidemic
             Simulation page. This runs the", code("netsim"), "function in EpiModel, and the
             epidemic parameters are described in detail in the help pages there.
             Model output may be plotted to show the epidemic time series or
             static network plots, as well as viewing numerical data summaries."),
           p("The author of this application is Emily Beylerian, Software Developer
             at the University of Washington Centers for Studies in Demoraphy and
             Ecology. The authors of the larger EpiModel project are Samuel Jenness,
             Steven Goodreau, and Martina Morris at the University of Washington.
             Development of this software is supported by the following grants from
             the National Institutes of Health: R01HD68395 (NICHD), T32HD007543 (NICHD),
             and R24HD042828 (NICHD).")
             )
           ),
  tabPanel("Network Model Estimation",
           tagList(
             tags$head(
               tags$link(rel = "stylesheet", type = "text/css", href = "style.css")
             )
           ),
           fluidRow(
             column(4,
                    br(),
                    wellPanel(
                      h4(tags$u("Network Model Estimation")),

                      actionButton("runMod", "Fit Model & Run Diagnostics",
                                   style = "margin-bottom: 10px;"),
                      fluidRow(
                        column(7, numericInput("num",
                                               label = "Number of Nodes",
                                               value = 100,
                                               min = 0))
                      ),

                      hr(),
                      h4("Specification: Method 1", style = "margin-bottom:0;"),
                      helpText("Summary Stat Targets", style = "margin-top:0;"),

                      sliderInput("meandeg",
                                  label = "Mean Degree",
                                  value = 0.5,
                                  min = 0.1,
                                  max = 1.5,
                                  step = 0.01),
                      sliderInput("meandur",
                                  label = "Mean Partnership Duration",
                                  value = 50,
                                  min = 1,
                                  max = 100),
                      selectInput("conc",
                                  label = "Concurrency Rule",
                                  choices = c("Concurrency not included in model",
                                              "Target % concurrency")),
                      conditionalPanel("input.conc == 'Target % concurrency'",
                                       helpText("Note: A mean degree greater than one always
                                                implies some level of concurrency. The model
                                                will not be run if concurrency is too low for
                                                the chosen mean degree."),
                                       uiOutput("percConcSlider")
                                       ),
                      hr(),
                      h4("Specification: Method 2", style = "margin-bottom:0;"),
                      helpText("Model and NW Stat Targets", style = "margin-top:0;"),
                      fluidRow(
                        column(7, selectInput("formation",
                                              label = "Formation Formula",
                                              choices = c("~edges", "~edges + concurrent"))),
                        column(5, numericInput("edge.target",
                                               label = "Target: edges",
                                               value = 25,
                                               step = 0.1),
                               conditionalPanel("input.formation == '~edges + concurrent'",
                                  numericInput("conc.target",
                                               label = "Target: concurrent",
                                               value = 10)
                                                )
                               )),

                      fluidRow(
                        column(7, selectInput("dissolution",
                                              label = "Dissolution Formula",
                                              choices = c("~offset(edges)"))),
                        column(5, numericInput("dur",
                                               label = "Edge Durations",
                                               value = 50)))
                    )), #end sidebar
             #main panel
             column(8,
                   br(),
                   fluidRow(
                     column(3, numericInput("dx.nsims",
                                            label = "Simulations",
                                            value = 5, min = 1),
                            actionButton("runDx",
                                         label = "Re-Run Diagnostics")),

                     column(3, numericInput("dx.nsteps",
                                            label = "Time Steps per Sim",
                                            value = 500, min = 1)),
                     column(3, selectInput("nwstats",
                                           label = "Network Stats to Track",
                                           multiple = TRUE,
                                           choices = c("edges",
                                                       "concurrent",
                                                       "isolates",
                                                       "mean degree" = "meandeg"),
                                           selected = "edges"))
                   ),
                   plotOutput("dxplot", height = "600px"),
                   wellPanel(
                     h4("Plot Options"),
                     fluidRow(
                       column(4,
                          selectInput("dxtype",
                               label = "Plot Type",
                               choices = c("formation", "dissolution", "duration"))),
                       column(5,
                              sliderInput(inputId = "dx.qntsrng",
                                          label = "Quantile Band",
                                          min = 0,
                                          max = 1,
                                          value = 0.5,
                                          step = 0.01))
                       ),
                       fluidRow(
                         column(3,
                                checkboxInput(inputId = "plots.joined",
                                              label = "Join Plots",
                                              value = TRUE)),
                         column(3,
                                checkboxInput(inputId = "dx.showmean",
                                              label = "Mean Line",
                                              value = TRUE)),
                         column(3,
                                checkboxInput(inputId = "dx.showsims",
                                              label = "Sim Lines",
                                              value = FALSE)),
                         column(3,
                                checkboxInput(inputId = "dx.showleg",
                                              label = "Legend",
                                              value = FALSE))),
                     fluidRow(
                       column(3,
                            downloadButton("dxplotDL", label = "Download Plot"))
                     )
                   ),

                   verbatimTextOutput("modeldx"))
           )
          ),
  tabPanel("Epidemic Simulation",
           fluidRow(
             column(4,
               br(),
               wellPanel(
                 h4(tags$u("Epidemic Simulation")),
                 actionButton("runEpi", label = "Simulate Epidemic",
                              style = "margin-bottom: 10px"),
                 helpText("Click the button above after changing model",
                          "parameters or conditions."),
                 selectInput("modtype",
                             label = "Disease Type",
                             choices = c("SI", "SIR", "SIS")),

                 h4("Initial Conditions", style = "margin-top: 25px;"),
                 numericInput(inputId = "i.num",
                              label = "Number Infected",
                              value = 1,
                              min = 0),
                 conditionalPanel("input.modtype == 'SIR'",
                                  numericInput(inputId = "r.num",
                                               label = "Number Recovered",
                                               value = 0,
                                               min = 0)),

                 h4("Time and Simulations", style = "margin-top: 25px;"),
                 numericInput("epi.nsims",
                              label = "Simulations",
                              value = 5, min = 1),
                 numericInput("epi.nsteps",
                              label = "Time Steps per Sim",
                              value = 500, min = 0),

                 h4("Parameters", style = "margin-top: 25px;"),
                 numericInput("inf.prob",
                              label = "Transmission Probability per Act",
                              min = 0,
                              max = 1,
                              value = 0.1,
                              step = 0.01),
                 numericInput(inputId = "act.rate",
                              label = "Act Rate",
                              min = 0,
                              value = 0.5,
                              step = 0.01),
                 conditionalPanel("input.modtype != 'SI'",
                                  numericInput(inputId = "rec.rate",
                                               label = "Recovery Rate",
                                               min = 0,
                                               value = 0,
                                               step = 0.01))
#                  numericInput(inputId = "b.rate",
#                               label = "Birth Rate",
#                               min = 0,
#                               value = 0.0,
#                               step = 0.005),
#                  numericInput(inputId = "ds.rate",
#                               label = "Death Rate (Sus.)",
#                               min = 0,
#                               value = 0.0,
#                               step = 0.005),
#                  numericInput(inputId = "di.rate",
#                               label = "Death Rate (Inf.)",
#                               min = 0,
#                               value = 0.0,
#                               step = 0.005),
#                  conditionalPanel("input.modtype == 'SIR'",
#                                   numericInput(inputId = "dr.rate",
#                                                label = "Death Rate (Rec.)",
#                                                min = 0,
#                                                value = 0.0,
#                                                step = 0.005))
               )), #end sidebar
             #main panel
             column(8,
                tabsetPanel(
                  tabPanel("Time Series Plots",
                     plotOutput("epiplot", height = "600px"),
                     wellPanel(
                       h4("Plot Options"),
                       fluidRow(
                         column(5,
                                selectInput(inputId = "compsel",
                                            label = strong("Plot Type"),
                                            choices = c("Compartment Prevalence",
                                                        "Compartment Size",
                                                        "Disease Incidence"))),
                         column(5,
                                sliderInput(inputId = "epi.qntsrng",
                                            label = "Quantile Band",
                                            min = 0,
                                            max = 1,
                                            value = 0.5,
                                            step = 0.01))
                         ),
                       fluidRow(
                         column(3,
                                checkboxInput(inputId = "epi.showmean",
                                              label = "Mean Line",
                                              value = TRUE)),
                         column(3,
                                checkboxInput(inputId = "epi.showsims",
                                              label = "Sim Lines",
                                              value = FALSE)),
                         column(3,
                                checkboxInput(inputId = "epi.showleg",
                                              label = "Legend",
                                              value = TRUE))),
                       fluidRow(
                         downloadButton("epiplotDL", "Download Plot")
                         )
                       )
                           ),
                  tabPanel("Network Plots",
                           uiOutput("plotUI"),
                           br(),
                           wellPanel(
                             h4("Plot Options"),
                             helpText("Plotting the mean network shows the plot
                                      of the simulation that is closest to
                                      the mean prevalence at each time step."),
                             checkboxInput("secondplot",
                                           label = "Plot two time steps",
                                           value = FALSE),
                             uiOutput("plotoptionsUI")
                             )
                           ),
                  tabPanel("Data",
                           div(style = "margin: auto; width: 90%;",
                               br(),
                               helpText("Select output as the time-specific means
                                      or standard deviations across simulations,
                                        or individual simulation values (if the
                                        last, also input the desired simulation
                                        number)."),
                               fluidRow(
                                 column(3,
                                        selectInput(inputId = "datasel",
                                                    label = strong("Data Selection"),
                                                    choices = c("Means",
                                                                "Standard Deviations",
                                                                "Simulations"))),
                                 conditionalPanel("input.datasel == 'Simulations'",
                                        column(3,
                                               uiOutput("simnoControl"))),
                                 column(3,
                                        numericInput(inputId = "tabdig",
                                                     label = "Significant Digits",
                                                     min = 0,
                                                     value = 2))),
                               fluidRow(
                                dataTableOutput("outData")),
                               fluidRow(
                             downloadButton(outputId = "dlData",
                                            label = "Download Data"))
                           )
                           ),
                  tabPanel("Summary",
                       br(),
                       uiOutput("sumtimeui"),
                       verbatimTextOutput("episum")
                           )
                )

              ) #end main panel
            )
           ) #end epi page
  )
)

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EpiModel documentation built on April 10, 2018, 9:05 a.m.