adding heading box + styling

app/app.R

@@ -35,6 +35,8 @@ admitsPanel <- function(prefix, tabtitle) {
   tabPanel(tabtitle, sidebarLayout(position = "left",
   sidebarPanel(
       chooseSliderSkin("Shiny", color = slider_color),
+      actionButton(fmtr("run"), "Run model", icon("play"),
+                   style = "align:right"),
       h2("Starting conditions", style = sprintf("color:%s", cmmid_color)),
       p("Data inputs specifying the starting point of the forecast: a number of new COVID-19 admissions on a given date at the location considered. Reporting refers to the % of admissions notified.",
         style = sprintf("color:%s", annot_color)),
@@ -56,6 +58,7 @@ admitsPanel <- function(prefix, tabtitle) {
           value = 100,
           step = 5
       ),
+      br(),
       h2("Model parameters", style = sprintf("color:%s", cmmid_color)),
       p("Parameter inputs specifying the COVID-19 epidemic growth as doubling time and associated uncertainty. Use more simulations to account for uncertainty in doubling time and length of hospital stay.",
         style = sprintf("color:%s", annot_color)),
@@ -93,9 +96,10 @@ admitsPanel <- function(prefix, tabtitle) {
           value = 30,
           step = 10
       ),
-  actionButton(fmtr("run"), "Run model", icon("play")), 
   ),
   mainPanel(
+      includeMarkdown("include/heading_box.md"),
+      br(),
       plotOutput(fmtr("main_plot"), width = "60%", height = "400px"),
       br(),
       checkboxInput(fmtr("show_los"), "Show duration of hospitalisation", FALSE),
@@ -123,8 +127,8 @@ ui <- navbarPage(
   windowTitle = app_title,
   theme = "styling.css",
   position="fixed-top", collapsible = TRUE,
-  admitsPanel(prefix="gen_", tabtitle="General"),
-  admitsPanel(prefix="icu_", tabtitle="ICU"),
+  admitsPanel(prefix = "gen_", tabtitle = "Non-critical care"),
+  admitsPanel(prefix = "icu_", tabtitle = "Critical care"),
   tabPanel("Overall", mainPanel(
     plotOutput("gen_over_plot"),
     br(),

app/include/heading_box.md

@@ -0,0 +1,27 @@
+
+
+<div class = "heading_box">
+
+<strong> <font size="+1">Welcome to the bed occupancy forecasting app</font></strong>
+<br>
+<strong>Summary</strong>
+<br>
+
+Forecast bed occupancy for COVID-19 patients using recent data on admission and
+an exponential growth model. You can choose to model critical or non-critical
+care beds using the two separate tabs. Use the panel on the left to input data
+and specify model parameters.
+
+
+<br>
+<br>
+<strong>About the model</strong> 
+<br> 
+
+Click on the information tab for details on the model, parameters used, and
+contact information. This app generalises the approach used in <a href =
+"https://cmmid.github.io/topics/covid19/current-patterns-transmission/ICU-projections.html"
+taget = "_blank"> this post</a> which focused only on critical care beds in
+England. Note that this work is under peer-review. Find us on <a href=https://github.com/thibautjombart/covid19_bed_occupancy/issues/ target = "_blank">github</a> for questions and requests. 
+
+</div>

app/include/info.md

@@ -3,18 +3,26 @@
 
 ## Model Description
 
-This app implements a model to forecast COVID19 bed requirements based on a
-starting point of admissions on a given date, a user-inputted epidemic growth
-rate, and an assumed distribution of length of stay after admission. This
-forecast can be adjusted to accommodate a certain assumed reporting percentage,
+This app can be used to forecast COVID-19 bed requirements for up to 21 days in
+a given location (e.g. a healthcare facility, a county, a state). Data on a
+number of reported admissions on a given date are used to define the starting
+point of the model. Future admissions are simulated using an exponential model
+and length of hospital stay for each new admitted patient is simulated using
+pre-specified distributions, depending on the type of hospitalisation (critical
+or non-critical care).
+
+Forecast can be adjusted to accommodate a certain assumed reporting percentage,
 if it is considered plausible that some hospitalised cases may not have their
 data reported. This may occur, for example, if admission rates are high and
 staff have limited opportunity to update the database with new records.
-
-
 Bed occupancies for non-critical care and critical care are modelled separately
 (see caveats).
 
+This app generalises a model used for predicting COVID-19 critical care bed requirements in
+England introduced in 
+[this post](https://cmmid.github.io/topics/covid19/current-patterns-transmission/ICU-projections.html).
+
+
 
 ### Summary
 

app/www/styling.css

@@ -71,3 +71,23 @@ hr { margin:5px; border-top: 1px solid grey; }
   box-shadow: 0 8px 16px 0 rgba(0,0,0,0.2), 0 6px 20px 0 rgba(0,0,0,0.19);
 }
 
+
+.heading_box {
+  margin-top: 0px;
+  margin-bottom: 20px;
+  padding: 20px 20px 15px 20px;
+  border: 1px solid #A7A8AA;
+  width: 70%;
+  box-shadow: 1px 1px 2px 2px #A7A8AA;
+  border-radius: 5px;
+}
+.heading_box p {
+  font-size: 16px;
+  font-color: #69716F;
+  line-height: 1;
+  margin: 0 0 6px;
+}
+.heading_box strong {
+  color: #0D5257;
+  margin: 0 0 10px;
+}