Overall Situation in Italy

Introduction
R Functions
This Week in Italy
Variations with respect to previous day
Situation in Italy
- Overall Situation
- Breakdown of Quarantine
Focus on Trentino, Liguria, Veneto and Lombardia
Situation by Region
- Situation by Region

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.

Introduction

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) {
    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:

# 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(
  "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	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)
diff_frame

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