Some Graphs about COVID-19 in Italy

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Introduction

This page contains various plots generated from that data using Org Mode and R: no fancy web services, just plain-old off-line generation. On top of being an interesting exercise on R and literate programming in Emacs, I use this page to get an idea of the evolution of the pandemic in Italy.

This page was created on <2020-03-28 Sat> and last updated on <2020-10-25 Sun>.

The source code available on the COVID-19 pages is distributed under the MIT License; the content is distributed under a Creative Commons - Attribution 4.0.

R Functions

This section contains the code for plotting data. The function my_plot plots different variables of an input dataframe over time, optionally filtering over region, which is the denomination of an Italian region.

The optional argument max defines the maximum value for the x-axis, while the optional Boolean arguments textlabels and filter control, respectively, whether text labels are printed on graphs and data has to be filtered by Region.

Finally, the optional arguments variables, graphtypes, and colors are vectors, defining, respectively, the variables to plot, the type of plot, and the colors used.

my_plot <- function(region, data, max=-1, textlabels=TRUE, filter=FALSE, 
                    variables  = c("totale_casi", "nuovi_positivi", "totale_positivi", "deceduti", "dimessi_guariti"),
                    graphtypes = c("l", "h", "l", "l", "l"),
                    colors     = c("red", "black", "orange", "slategrey", "forestgreen")) {
  par(cex=1.40, las=2)

  # if asked to filter, filter data according to region
  if (filter) {
    dataframe <- subset(data, denominazione_regione == region)
  }
  else {
    dataframe <- data
  }

  if (max == -1) {
    max=max(dataframe$totale_casi)
  }

  plot(x=1, 
       xlim=c(min(data$data), max(data$data)),
       ylim=c(0,max),
       type="n",
       main = region,
       xlab="",
       ylab="",
       xaxt="n")

  axis.Date(1, at=dataframe$data, by="days", format="%b %d")

  # do the plots, now
  for (i in 1:length(variables)) {
    lines(x=dataframe$data, y=dataframe[, variables[i]],
          type=graphtypes[i], 
          lwd=5,
          pch=16, 
          col=colors[i])
    if (textlabels) {
      text(x=dataframe$data, y=dataframe[, variables[i]], 
           label=dataframe[, variables[i]], 
           pos=2, 
           col=colors[i])
    }
  }

  values = sprintf("(%s)", dataframe[nrow(dataframe), variables])
  legend("topleft", legend=paste(variables, values), col=colors, lty=1, cex=1.6)
  grid(col = "lightgray")
}

Then we read the data from the CSV files of the Civil Protection repository:

PATH="/home/adolfo/Downloads/COVID-19/"

# evolution over time, by Region
data = read.csv(file.path(PATH, "dati-regioni/dpc-covid19-ita-regioni.csv"))
data$data <- as.Date(data$data)

# evolution over time at the National level
national = read.csv(file.path(PATH, "dati-andamento-nazionale/dpc-covid19-ita-andamento-nazionale.csv"))
national$data <- as.Date(national$data)

# latest regional data
latest = read.csv(file.path(PATH, "dati-regioni/dpc-covid19-ita-regioni-latest.csv"))
latest$data <- as.Date(national$data)

We are now ready to print and plot the data.

This Week in Italy

cols = c(
  "ricoverati_con_sintomi", 
  "terapia_intensiva",
  "totale_ospedalizzati",
  "isolamento_domiciliare", 
  "totale_positivi",
  "nuovi_positivi",
  "dimessi_guariti",
  "deceduti",
  "totale_casi"
)
labels = c(
  "In hospitals with symptoms", 
  "In ICUs",
  "Total hospitalized",
  "Quarantined at home", 
  "Active cases",
  "New cases",
  "Recovered",
  "Deaths",
  "Total number of cases"
)

Today = unlist(national[nrow(national), cols])
Yesterday = unlist(national[nrow(national) - 1, cols])
TwoDaysAgo = unlist(national[nrow(national) - 2, cols])
ThreeDaysAgo = unlist(national[nrow(national) - 3, cols])
FourDaysAgo = unlist(national[nrow(national) - 4, cols])
FiveDaysAgo = unlist(national[nrow(national) - 5, cols])

output_frame <- data.frame(labels, FiveDaysAgo, FourDaysAgo, ThreeDaysAgo, TwoDaysAgo, Yesterday, Today)
colnames(output_frame) <- rev(seq(Sys.Date(), by="-1 day", length.out=7))
colnames(output_frame)[1] <- "Label"
output_frame
Label 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26
In hospitals with symptoms 9057 9694 10549 11287 12006 12997
In ICUs 926 992 1049 1128 1208 1284
Total hospitalized 9983 10686 11598 12415 13214 14281
Quarantined at home 145459 158616 174404 190767 209027 222403
Active cases 155442 169302 186002 203182 222241 236684
New cases 15199 16079 19143 19644 21273 17012
Recovered 257374 259456 261808 264117 266203 268626
Deaths 36832 36968 37059 37210 37338 37479
Total number of cases 449648 465726 484869 504509 525782 542789

Variations with respect to previous day

We now plot the variations in the last week, that is the difference between a day and the previous day. In many cases, the lower the number, the better. In other cases (e.g., Recovered), the higher, the better.

Diff4 = FourDaysAgo - FiveDaysAgo
Diff3 = ThreeDaysAgo - FourDaysAgo
Diff2 = TwoDaysAgo - ThreeDaysAgo 
Diff1 = Yesterday - TwoDaysAgo
Diff0 = Today - Yesterday

diff_frame <- data.frame(labels, Diff4, Diff3, Diff2, Diff1, Diff0)
diff_frame
labels Diff4 Diff3 Diff2 Diff1 Diff0
In hospitals with symptoms 637 855 738 719 991
In ICUs 66 57 79 80 76
Total hospitalized 703 912 817 799 1067
Quarantined at home 13157 15788 16363 18260 13376
Active cases 13860 16700 17180 19059 14443
New cases 880 3064 501 1629 -4261
Recovered 2082 2352 2309 2086 2423
Deaths 136 91 151 128 141
Total number of cases 16078 19143 19640 21273 17007

See also the historical series of new cases in Italy.

Situation in Italy

Overall Situation

Evolution over time.

my_plot("Italia", national, textlabels=FALSE)

italia.png

Breakdown of Quarantine

It tells where people with COVID-19 are spending their quarantine, that is, a breakdown of the “yellow” line of the previous plot.

The blue line is the number of people hospedalized during the (first) lockdown. Now the capacity of the health system should be higher, but it seems something to look at (although the situation differs from region to region).

  my_plot("Italia", 
          national,
          max(national$isolamento_domiciliare), textlabels=FALSE, filter=FALSE, 
          variables=c("ricoverati_con_sintomi", "terapia_intensiva", "totale_ospedalizzati", "isolamento_domiciliare"),  
          graphtypes=c("l", "l", "l", "l", "h"),
          colors=c("#FECEAB", "#EC2049", "#E84A5F", "#A7226E"))
abline(h = new_national[38,]$totale_ospedalizzati, col="#0000FF", lwd=3)
abline(v = as.Date("2020-10-25"), col="#0000FF", lwd=3)

hospitalized.png

Focus on Trentino, Liguria, Veneto and Lombardia

Situation in Trentino

my_plot("P.A. Trento", data, filter=TRUE, textlabels=FALSE)

trentino.png

Situation in Liguria

my_plot("Liguria", data, filter=TRUE, textlabels=FALSE)

liguria.png

Situation in Veneto

my_plot("Veneto", data, filter=TRUE, textlabels=FALSE)

veneto.png

Situation in Lombardia

my_plot("Lombardia", data, filter=TRUE, textlabels=FALSE)

lombardia.png

Situation by Region

Situation by Region

# how many rows and columns?
par(mfrow=c(11, 2))

max <- max(data$totale_casi)

regions <- c("Valle d'Aosta", "Piemonte", "Liguria", "Lombardia", "Veneto",
             "P.A. Trento", "P.A. Bolzano", "Friuli Venezia Giulia",
             "Emilia-Romagna", "Toscana", "Marche", "Umbria",
             "Lazio", "Abruzzo", "Molise", "Campania",
             "Puglia", "Basilicata", "Calabria", "Sicilia",
             "Sardegna")
for (region in regions) {
  my_plot(
    region, data, filter=TRUE, textlabels=FALSE,
          variables=c("totale_casi", "totale_positivi", "deceduti", "dimessi_guariti"),
          max = max,
          graphtypes=c("l", "l", "l", "l"),
          colors=c("red", "orange", "slategrey", "forestgreen"))
}

cases_by_region.png

Author: Adolfo Villafiorita

Last modified: 2020-10-25 Sun 22:21 (created on: 2020-03-28 Sat 00:00)

Published: 2020-10-26 Mon 18:26