---
title: "Coronavirus in Denmark"
author: "Christian Heebøll-Nielsen"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE,}
knitr::opts_chunk$set(echo = FALSE)
#------------------ Packages ------------------
#devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(flexdashboard)
library(lubridate)
library(ggplot2)
library(MASS)
library(magrittr)
library(dplyr)
library(zoo)
library(easynls)
library(readxl)
library(plotly)
library(growthmodels)
library(R0)
library(readr)

data <- read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", na.strings = "", fileEncoding = "UTF-8-BOM")

data <- data %>% 
    group_by(countriesAndTerritories) %>% 
    mutate(date = make_date(year, month, day))

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- data %>%
  dplyr::filter(countriesAndTerritories == "Denmark") %>%
  dplyr::summarise(total_cases = sum(cases), total_deaths = sum(deaths))
  
df_daily <- data %>%
  dplyr::filter(countriesAndTerritories == "Denmark") %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(cases),
    death_cum = cumsum(deaths),
  )


df1 <- data %>% dplyr::filter(date == max(date))
```

Summary
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

 valueBox(
  value = paste(format(sum(df$total_cases), big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-user-md",
  color = confirmed_color
  )
```


### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$total_deaths, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$total_deaths, na.rm = TRUE) / sum(df$total_cases), 1),
    "%)",
    sep = ""
  ),
  caption = "Death cases (death rate)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


Row
-----------------------------------------------------------------------

### **Daily cumulative cases by type** (Denmark only)
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmed", secondary_y = FALSE,
    line = list(color = active_color),
    marker = list(color = active_color),
    yaxis = 'y'
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Death", secondary_y = TRUE,
    line = list(color = death_color),
    marker = list(color = death_color),
    yaxis = 'y2'
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-02-27"),
    y = 1,
    text = paste("First case"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-14"),
    y = 3,
    text = paste("First death"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -50
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-18"),
    y = 14,
    text = paste(
      "Lockdown"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -30,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-04-15"),
    y = 14,
    text = paste(
      "Schools reopen"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -50
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-04-20"),
    y = 14,
    text = paste(
      "Reopening liberal business"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 0,
    ay = -70
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-05-11"),
    y = 14,
    text = paste(
      "Reopening retail"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 10,
    ay = -50
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-05-18"),
    y = 14,
    text = paste(
    "Older children back to school, churches, libraries, cafes, restaurants "
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 50,
    ay = 50
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Cumulative number of cases"),
    yaxis2 = list(overlaying = "y", side = "right", position = 0.95, title = "Cumulative number of deaths"),
    xaxis = list(title = "Date"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Comparison
=======================================================================


### **Daily new confirmed deaths - moving average**
    
```{r}

data1 <- data %>% 
    group_by(countriesAndTerritories) %>% 
    filter(countryterritoryCode == "DNK" | countryterritoryCode == "SWE" | countryterritoryCode == "NOR") %>%
    mutate(date = make_date(year, month, day))

daily_deaths <- data1 %>%
    mutate(rollingmean = c(rep(NA, 3),  # Adding a rolling mean
                         zoo::rollmean(x = deaths, k = 7), 
                         rep(NA, 3))) %>%
  dplyr::filter(date >= "2020-03-01") %>%
  dplyr::mutate(country = countriesAndTerritories) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(rollingmean)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)


#----------------------------------------
# Plotting the data
daily_deaths %>%
  
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Denmark,
    type = "scatter",
    mode = "lines+markers",
    name = "Denmark"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Sweden,
    type = "scatter", 
    mode = "lines+markers",
    name = "Sweden"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Norway,
    type = "scatter",
    mode = "lines+markers",
    name = "Norway"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of new confirmed deaths"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


R - values
=======================================================================

Column {data-width=400}
-------------------------------------

### **Denmark**
```{r}
knitr::opts_chunk$set(echo = FALSE) 
library(R0)
library(dplyr)
# set the serial interval to a lognormal distribution with a mean of 4.8 and a spread of 2.3
mGT <- generation.time("lognormal", c(4.7, 2.9)) 
# https://www.researchgate.net/publication/339705453_Serial_interval_of_novel_coronavirus_COVID-19_infections

Test_pos_over_time <- read_delim("Test_pos_over_time.csv", 
                                 ";", escape_double = FALSE, trim_ws = TRUE)

# hosp_raw <- read_delim("Newly_admitted_over_time.csv", 
# ";", escape_double = FALSE, trim_ws = TRUE)
# incidence <- hosp_raw$Total

incidence <- Test_pos_over_time$NewPositive
incidence <- incidence[150:length(incidence)-2]
    
    end <- as.integer(length(incidence) - 5)
    begin <- as.integer(1)
    
    r <- est.R0.TD(incidence, GT = mGT, begin = begin, end = end,
                           date.first.obs = Test_pos_over_time$Date[150])
     
    epid <- r$epid$t[1:(length(incidence) - 5)]
    
    conf <- r$conf.int
    
    upper <- r$conf.int$upper
    
    lower <- r$conf.int$lower
    
    df_conf <- data.frame(epid, upper, lower, r$R)
    
    a <- list(
      text = "Baseret på nye positive test per dr$ag",
      xref = "paper",
      yref = "paper",
      yanchor = "bottom",
      xanchor = "left",
      align = "right",
      x = 0,
      y = 0,
      showarrow = FALSE
    )
    
    fig <- plot_ly(df_conf, x = ~epid, y = ~upper, type = 'scatter', mode = 'lines',
                   line = list(color = 'transparent'),
                   showlegend = FALSE, name = "confidence interval") 
    
    fig <- fig %>% add_trace(y = ~lower, type = 'scatter', mode = 'lines',
                            fill = 'tonexty', fillcolor = 'rgba(0,100,80,0.2)', line = list(color = 'transparent'),
                   showlegend = FALSE, name = "confidence interval") 
    
    fig <- fig %>% add_trace(x = ~epid, y = ~r$R, type = 'scatter', mode = 'lines',
                             line = list(color = 'rgb(0,100,80)'),
                             name = 'R value')
    
    fig <- fig %>% layout(title = "R values - Denmark", annotations = a,
                          paper_bgcolor = 'rgb(255,255,255)', plot_bgcolor = 'rgb(229,229,229)',
                          xaxis = list(title = "Date",
                                       gridcolor = 'rgb(255,255,255)',
                                       showgrid = TRUE,
                                       showline = FALSE,
                                       showticklabels = TRUE,
                                       tickcolor = 'rgb(127,127,127)',
                                       ticks = 'outside',
                                       zeroline = FALSE),
                          yaxis = list(title = "R value",
                                       gridcolor = 'rgb(255,255,255)',
                                       showgrid = TRUE,
                                       showline = FALSE,
                                       showticklabels = TRUE,
                                       tickcolor = 'rgb(127,127,127)',
                                       ticks = 'outside',
                                       range = c(0,4),
                                       zeroline = FALSE))
    
    fig

```

### **Sweden**
```{r}
knitr::opts_chunk$set(echo = FALSE)
url <- "https://www.arcgis.com/sharing/rest/content/items/b5e7488e117749c19881cce45db13f7e/data"

# Download Excel-file to temp file
xls_file <- tempfile()
utils::download.file(url, xls_file, mode = "wb")

# Read Excel-file and extract data from the sheet named "Antal intensivvårdade per dag"
ICU <- read_excel(
  xls_file,
  sheet = "Antal intensivvårdade per dag",
  col_names = c("ICU_date","ICU"),
  col_types = c("date","numeric"),
  skip = 1)

mGT <- generation.time("lognormal", c(4.7, 2.9)) #https://www.researchgate.net/publication/339705453_Serial_interval_of_novel_coronavirus_COVID-19_infections

incidenceS <- ICU$ICU[111:length(ICU$ICU)]
endS <- as.integer(length(incidenceS) - 7)
beginS <- as.integer(111)

rs <- est.R0.TD(incidenceS, GT = mGT, begin = beginS, end = endS,
                       date.first.obs = ICU$ICU_date[111])
      
epidS <- rs$epid$t[1:(length(incidenceS) - 7)]

confS <- rs$conf.int

upperS <- rs$conf.int$upper

lowerS <- rs$conf.int$lower

df_confS <- data.frame(epidS, upperS, lowerS, rs$R)

a <- list(
  text = "Based on Antal intensivvårdade per dag",
  xref = "paper",
  yref = "paper",
  yanchor = "bottom",
  xanchor = "left",
  align = "right",
  x = 0,
  y = 0,
  showarrow = FALSE
)

fig <- plot_ly(df_confS, x = ~epidS, y = ~upperS, type = 'scatter', mode = 'lines',
               line = list(color = 'transparent'),
               showlegend = FALSE, name = "confidence interval") 

fig <-fig %>% add_trace(y = ~lowerS, type = 'scatter', mode = 'lines',
                        fill = 'tonexty', fillcolor='rgba(0,100,80,0.2)', line = list(color = 'transparent'),
               showlegend = FALSE, name = "confidence interval") 

fig <- fig %>% add_trace(x = ~epidS, y = ~rs$R, type = 'scatter', mode = 'lines',
                         line = list(color='rgb(0,100,80)'),
                         name = 'R value')

fig <- fig %>% layout(title = "R values - Sweden", annotations = a,
                      paper_bgcolor='rgb(255,255,255)', plot_bgcolor='rgb(229,229,229)',
                      xaxis = list(title = "Date",
                                   gridcolor = 'rgb(255,255,255)',
                                   showgrid = TRUE,
                                   showline = FALSE,
                                   showticklabels = TRUE,
                                   tickcolor = 'rgb(127,127,127)',
                                   ticks = 'outside',
                                   zeroline = FALSE),
                      yaxis = list(title = "R value",
                                   gridcolor = 'rgb(255,255,255)',
                                   showgrid = TRUE,
                                   showline = FALSE,
                                   showticklabels = TRUE,
                                   tickcolor = 'rgb(127,127,127)',
                                   ticks = 'outside',
                                   range = c(0,4),
                                   zeroline = FALSE))

fig

```

Development
=======================================================================

Column {data-width=400}
-------------------------------------

### **Denmark - new positive tests**
```{r}
library(tidyverse)
#url <- "https://api.covid19data.dk/ssi_newly_hospitalized"

#hosp_raw <- jsonlite::fromJSON(url)

#hosp_raw <- hosp_raw %>%
# as_tibble() %>%
# mutate(date = date %>% lubridate::ymd_hms() %>% as.Date())

## Do data transformations #####################################################

# The last four days are usually not fully up-to-date and are missing 
# hospitalizations. So we will cut the last four entries and create a 7-day 
# rolling mean

#ads <- hosp_raw %>%
#  slice(1:(n()-4)) %>% # remove last four entries
#  mutate(rollingmean = c(rep(NA, 3),  # Adding a rolling mean
#                         zoo::rollmean(x = newly_hospitalized, k = 7), 
#                         rep(NA, 3)))

# ads %>% 
#  ggplot() + 
#  geom_bar(aes(date, newly_hospitalized), stat = "identity", fill = "Purple") + 
#  geom_line(aes(date, rollingmean)) +
#  theme_minimal()
  
  plotfit(r)

```

### **Sweden - new ICU patients**
```{r}
  plotfit(rs)
```

Death projections
=======================================================================

Column {data-width=400}
-------------------------------------

### **Denmark**
```{r}

Denmark <- data %>% 
    filter(countryterritoryCode == "DNK" & date > "2020-03-13") %>%
    arrange(date) %>%
    mutate(dayNo = (1:length(date))) %>%
    mutate(cumdeaths = cumsum(deaths))

df_Danmark <- data.frame(Denmark$dayNo, Denmark$cumdeaths)

nls_DK <- nlsfit(df_Danmark, model = 10, start = c(500, 10, 0.05))

a <- nls_DK$Parameters["coefficient a", "Denmark.cumdeaths"]
b <- nls_DK$Parameters["coefficient b", "Denmark.cumdeaths"]
c <- nls_DK$Parameters["coefficient c", "Denmark.cumdeaths"]
t <- 1:200

#Plot actual deaths in green, prognosis in red
plot(gompertz(t, a, b, c), col="red",  xlim=c(0, 200), ylim=c(0,1000), main="Forecast of deaths, Denmark", xlab="Day", ylab="Deaths")
abline(h=nls_DK$Parameters["coefficient a", "Denmark.cumdeaths"], col="red")
text(10, 800, labels=as.integer(nls_DK$Parameters["coefficient a", "Denmark.cumdeaths"]), cex=2)

par(new=TRUE)

plot(Denmark$dayNo, Denmark$cumdeaths, xlim=c(0, 200), ylim=c(0,1000), col="green", xlab="Day", ylab="Deaths")
legend("topright", legend=c("Forecast", "Deaths"),
       col=c("red", "green"), lty=1:2, cex=0.8)

```

### **Sweden**
```{r}

Sweden <- data %>% 
    filter(countryterritoryCode == "SWE" & date > "2020-03-13") %>%
    arrange(date) %>%
    mutate(dayNoS = (1:length(date))) %>%
    mutate(cumdeaths = cumsum(deaths))

df_Sweden <- data.frame(Sweden$dayNoS, Sweden$cumdeaths)
library(easynls)
nls_SW <- nlsfit(df_Sweden, model = 10, start = c(4000, 10, 0.05))

as <- nls_SW$Parameters["coefficient a", "Sweden.cumdeaths"]
bs <- nls_SW$Parameters["coefficient b", "Sweden.cumdeaths"]
cs <- nls_SW$Parameters["coefficient c", "Sweden.cumdeaths"]
ts <- 1:200

#Plot actual deaths in green, prognosis in red
plot(gompertz(ts, as, bs, cs), col="red",  xlim=c(0, 200), ylim=c(0,6000), main="Forecast of deaths, Sweden", xlab="Day", ylab = "Deaths")
abline(h=nls_SW$Parameters["coefficient a", "Sweden.cumdeaths"], col="red")
text(10, 5000, labels=as.integer(nls_SW$Parameters["coefficient a", "Sweden.cumdeaths"]), cex=2)
par(new = TRUE)
plot(Sweden$dayNoS, Sweden$cumdeaths, xlim=c(0, 200), ylim=c(0,6000), col="green", xlab="Day", ylab="Deaths")
legend("topright", legend = c("Forecast", "Deaths"),
       col = c("red", "green"), lty = 1:2, cex = 0.8)
```


About
=======================================================================

**The Coronavirus Dashboard: the case of Denmark**

This [Coronavirus dashboard: the case of Denmark](https://www.antoinesoetewey.com/files/coronavirus-dashboard.html) provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Denmark. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.

**Code**

The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.

**Data**

The data and dashboard are refreshed on a daily basis.

The raw data is pulled from the ECDC read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", na.strings = "", fileEncoding = "UTF-8-BOM").

**Information**

More information about this dashboard (and how to replicate it for your own country) can be found in this [article](https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).

**Update**

The data is as of `r format(max(data$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.



*Go back to [www.statsandr.com](https://www.statsandr.com/) (blog) or [www.antoinesoetewey.com](https://www.antoinesoetewey.com/) (personal website)*.