TestReport2.Rmd

---
title: |
  <center> CHAD Output Summary </center>
subtitle: |
  <center> `r format(Sys.time(), '%d %B, %Y')` </center>
  
author: 
date:
output: html_document

---


---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r echo = FALSE, warning = FALSE, message = FALSE}
# Some global stuff
library(ggplot2)
library(plotly)
library(knitr)
library(kableExtra)

Counties = GetCounties(input$Base, input$Radius)
Hospitals = GetHospitals(input$Base, input$Radius)
CountyDT = GetLocalDataTable(Counties)
CovidCounties = subset(CovidConfirmedCases, CountyFIPS %in% Counties$FIPS)
CountyCovid = CalculateCovid(Counties)
CountyDeaths = CalculateDeaths(Counties)
TotPop = CalculateCounties(Counties)
CovidCaseChng = sum(rev(CovidCounties)[,1] - rev(CovidCounties)[,2])
CovidDeathChng <- sum(rev(CovidCounties)[,1] - rev(CovidCounties)[,2])
HospUltz = HospitalIncreases(Counties, Hospitals)
HospUltz2 = HospitalUtlzChng(input$Base, input$Radius)
```

```{r echo=FALSE}

OverlayDatahosp = PlotOverlay2(input$Base, 
                      Counties, 
                      Hospitals, 
                      input$social_dist, 
                      input$proj_days,
                      "Hospitalizations")

overlayDF7h <- dplyr::filter(OverlayDatahosp, ForecastDate == Sys.Date()+7)
overlayDF14h <- dplyr::filter(OverlayDatahosp, ForecastDate == Sys.Date()+14)
overlayDF30h <- dplyr::filter(OverlayDatahosp, ForecastDate == Sys.Date()+30)

OverlayDatafatal = PlotOverlay2(input$Base, 
                      Counties, 
                      Hospitals, 
                      input$social_dist, 
                      input$proj_days,
                      "Deaths")

overlayDF7f <- dplyr::filter(OverlayDatafatal, ForecastDate == Sys.Date()+7)
overlayDF14f <- dplyr::filter(OverlayDatafatal, ForecastDate == Sys.Date()+14)
overlayDF30f <- dplyr::filter(OverlayDatafatal, ForecastDate == Sys.Date()+30)

```
---





<center>
#  `r input$Base` 
</center>

The Air Force COVID-19 Health Assessment Dashboard (CHAD) was developed to provide a centralized location for leadership to receive up-to-date pandemic information specifically tailored to their installation and the surrounding local area. 

This report was generated using a **`r input$Radius` mile radius** around **`r input$Base`** as the defined local area.




## Current Local Area Info

<style>
div.blue { background-color:#e6f0ff; border-radius: 5px; padding: 20px;}
</style>
<div class = "blue">

<div class = "row">

<div class = "col-md-5">

Local Population: 

**`r comma(TotPop)`**

Total Cases:

**`r comma(CalculateCovid(Counties))`**

New Cases: 

**`r paste("+",comma(CovidCaseChng))`**

Total Fatalities: 

**`r comma(CalculateDeaths(Counties))`**

New Fatalities: 

**`r paste("+",comma(CovidDeathChng))`**

Estimated Hospital Bed Utilization:

**`r HospUltz`**

Estimated Hospital Bed Utilization Change:

**`r HospUltz2`**



</div>

<div class = "col-md-7">

```{r fig.height = 4, fig.width = 5, echo=FALSE}
CountyChoro = PlotLocalChoro(Counties, input$Base, "County")
CountyChoro
```

</div>
</div>

## Local Area Plots

<br>

### Daily Stats

The charts below display daily values and cumulative values of cases, hospitalizations, and fatalities

<div class = "row">

<div class = "col-md-6">
```{r fig.height = 4, fig.width = 4, echo=FALSE}
CCDaily = CovidCasesPerDayChart(input$Base, input$Radius, Counties, Hospitals)
CCDaily = CCDaily %>% layout(xaxis = list(showgrid = T),
                             yaxis = list(showgrid = T))
CCDaily
```
</div>

<div class = "col-md-6">
```{r fig.height = 4, fig.width = 4, echo=FALSE}
CCtot = CovidCasesCumChart(input$Base, input$Radius, Counties, Hospitals)
CCtot = CCtot %>% layout(xaxis = list(showgrid = T),
                         yaxis = list(showgrid = T))
CCtot
```
</div>

</div>



### Local Projections

This section will display the projections from the IHME model and the CHIME inspired model on the same graph for daily hospitalizations and cumulative fatalities. Following each chart will be a table showing the 7, 14, and 30 day projections for each model.

<br>

```{r fig.height = 5, fig.width = 10,echo=FALSE}

overlay = PlotOverlay(input$Base, 
                      Counties, 
                      Hospitals, 
                      input$social_dist, 
                      input$proj_days,
                      "Hospitalizations")

overlay

```

<br>


<style>
div.blue { background-color:#e6f0ff; border-radius: 5px; padding: 20px;}
</style>
<div class = "blue">



<div class = "row">

<div class = "col-md-4">

<div class = "row">
<center>**7-Day Daily Hospitalization Projection**</center>
</div>


<div class = "row">


<div class = "col-md-6">

<center>IHME</center>

<center>`r (dplyr::filter(overlayDF7h, ID == "IHME"))[,2]`</center>

</div>


<div class = "col-md-6">

<center>CHIME</center>

<center>`r (dplyr::filter(overlayDF7h, ID == "CHIME"))[,2]`</center>

</div>

</div>


</div>




<div class = "col-md-4">

<div class = "row">
<center>**14-Day Daily Hospitalization Projection**</center>
</div>


<div class = "row">


<div class = "col-md-6">

<center>IHME</center>

<center>`r (dplyr::filter(overlayDF14h, ID == "IHME"))[,2]`</center>

</div>


<div class = "col-md-6">

<center>CHIME</center>

<center>`r (dplyr::filter(overlayDF14h, ID == "CHIME"))[,2]`</center>

</div>

</div>


</div>



<div class = "col-md-4">

<div class = "row">
<center>**30 Day Daily Hospitalization Projection**</center>
</div>


<div class = "row">


<div class = "col-md-6">

<center>IHME</center>

<center>`r (dplyr::filter(overlayDF30h, ID == "IHME"))[,2]`</center>

</div>


<div class = "col-md-6">

<center>CHIME</center>

<center>`r (dplyr::filter(overlayDF30h, ID == "CHIME"))[,2]`</center>

</div>

</div>


</div>



</div>
</div>


<br><br>

```{r fig.height = 5, fig.width = 9,echo=FALSE}

overlay = PlotOverlay(input$Base, 
                      Counties, 
                      Hospitals, 
                      input$social_dist, 
                      input$proj_days,
                      "Deaths")
overlay
```


<br>


<style>
div.blue { background-color:#e6f0ff; border-radius: 5px; padding: 20px;}
</style>
<div class = "blue">



<div class = "row">

<div class = "col-md-4">

<div class = "row">
<center>**7-Day Total Fatality Projection**</center>
</div>


<div class = "row">


<div class = "col-md-6">

<center>IHME</center>

<center>`r (dplyr::filter(overlayDF7f, ID == "IHME"))[,2]`</center>

</div>


<div class = "col-md-6">

<center>CHIME</center>

<center>`r (dplyr::filter(overlayDF7f, ID == "CHIME"))[,2]`</center>

</div>

</div>


</div>




<div class = "col-md-4">

<div class = "row">
<center>**14-Day Total Fatality Projection**</center>
</div>


<div class = "row">


<div class = "col-md-6">

<center>IHME</center>

<center>`r (dplyr::filter(overlayDF14f, ID == "IHME"))[,2]`</center>

</div>


<div class = "col-md-6">

<center>CHIME</center>

<center>`r (dplyr::filter(overlayDF14f, ID == "CHIME"))[,2]`</center>

</div>

</div>


</div>



<div class = "col-md-4">

<div class = "row">
<center>**30 Day Total Fatality Projection**</center>
</div>


<div class = "row">


<div class = "col-md-6">

<center>IHME</center>

<center>`r (dplyr::filter(overlayDF30f, ID == "IHME"))[,2]`</center>

</div>


<div class = "col-md-6">

<center>CHIME</center>

<center>`r (dplyr::filter(overlayDF30f, ID == "CHIME"))[,2]`</center>

</div>

</div>


</div>



</div>
</div>


***
***


### Detailed County Info

This table displays information for each county within `r input$Radius` miles of `r input$Base`.


```{r echo=FALSE}
kable(CountyDT) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
```