Overall Situation in Italy

Table of Contents


After two years, I finally decided to stop updating this page and evaluation of all code blocks has been disabled. The data was last updated on October 28/2022.


This page contains various plots generated 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 regularly updated since then.

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) {

       xlim=c(min(data$data), max(data$data)),
       main = region,

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

  # do the plots, now
  for (i in 1:length(variables)) {
    lines(x=dataframe$data, y=dataframe[, variables[i]],
    if (textlabels) {
      text(x=dataframe$data, y=dataframe[, variables[i]], 
           label=dataframe[, variables[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:

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

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

# latest regional data
latest = read.csv(file.path(PATH, "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(
labels = c(
  "In hospitals with symptoms", 
  "In ICUs",
  "Total hospitalized",
  "Quarantined at home", 
  "Active cases",
  "New cases",
  "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"
Label 2022-10-24 2022-10-25 2022-10-26 2022-10-27 2022-10-28 2022-10-29
In hospitals with symptoms 7017 7124 7106 7019 6881 6824
In ICUs 229 226 232 227 223 228
Total hospitalized 7246 7350 7338 7246 7104 7052
Quarantined at home 512551 501090 492661 491023 477137 468854
Active cases 519797 508440 499999 498269 484241 475906
New cases 25554 11606 48714 35043 31760 29040
Recovered 22649684 22672607 22729641 22766314 22812006 22849293
Deaths 178594 178633 178753 178846 178940 179025
Total number of cases 23348075 23359680 23408393 23443429 23475187 23504224

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)
labels Diff4 Diff3 Diff2 Diff1 Diff0
In hospitals with symptoms 107 -18 -87 -138 -57
In ICUs -3 6 -5 -4 5
Total hospitalized 104 -12 -92 -142 -52
Quarantined at home -11461 -8429 -1638 -13886 -8283
Active cases -11357 -8441 -1730 -14028 -8335
New cases -13948 37108 -13671 -3283 -2720
Recovered 22923 57034 36673 45692 37287
Deaths 39 120 93 94 85
Total number of cases 11605 48713 35036 31758 29037

See also the historical series of new cases in Italy.

Situation in Italy

Overall Situation

Evolution over time.

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


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).

          max(national$isolamento_domiciliare), textlabels=FALSE, filter=FALSE, 
          variables=c("ricoverati_con_sintomi", "terapia_intensiva", "totale_ospedalizzati", "isolamento_domiciliare"),  
          graphtypes=c("l", "l", "l", "l", "h"),
          colors=c("#FECEAB", "#EC2049", "#E84A5F", "#A7226E"))
abline(h = national[38,]$totale_ospedalizzati, col="#330000", lwd=2, lty=3)
abline(v = as.Date("2020-10-25"), col="#330000", lwd=2, lty=3)


Focus on Trentino, Liguria, Veneto and Lombardia

Situation in Trentino

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


Situation in Liguria

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


Situation in Veneto

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


Situation in Lombardia

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


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",
for (region in regions) {
    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"))


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.