New Cases in Italy

Table of Contents

Introduction
R Functions
New Cases in Italy
- Line Plot
- Calendar Heatmap
New Cases by Region
- Tabular Data
- Line Plots

Introduction

This page focuses on new cases in Italy. This page was created on <2020-08-01 Sat> and regularly updated since then.

R Functions

First we read the data from the CSV files of the Civil Protection repository¹:

We get it into R:

# 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
new_national = read.csv(file.path(PATH, "dpc-covid19-ita-andamento-nazionale.csv"))
new_national$data <- as.Date(new_national$data)

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

We then output a table of new cases from R, which we then use in Ruby. (The table is not exported to HTML.)

Inferior Ruby shells perform very badly with long input lines. Make sure you don’t pass the variable to a Ruby :session or the evaluation will break, while IRB tries to cope with the declaration of the input variable.

If you need a :session, you should read data directly from the Ruby session, rather than passing it as a variables.

new_national[, c("data", "nuovi_positivi")]

New Cases in Italy

Line Plot

The shaded area corresponds to the lockdown. The red horizontal line allows to compare the latest data with historical data.

p = plot(new_national$nuovi_positivi ~ new_national$data, type="l", lwd=6, pch=16, cex=1.6, col=c("#3B3176"))
#text(new_national$nuovi_positivi ~ new_national$data, labels=new_national$nuovi_positivi, pos=2, cex=1.3)
#abline(v = as.Date("2020-10-25"), col="#FF0000", lwd=3)

grid(p, col = "black", lty="dotted")
#rect(as.Date("2020-03-09"), -200, as.Date("2020-05-19"), 7000, border = "#AA0055", col = "#AA0055", density=2)

text(x = as.Date("2020-03-25"), y = 8000, "First Wave", col="#FF0000")
abline(h = new_national[27,]$nuovi_positivi, col="#FF0000", lwd=2)
#abline(v = as.Date("2020-03-01"), col="#FF0000", lwd=2)

abline(h = 41000, col="#FF0000", lwd=2)
text(x = as.Date("2020-11-15"), y = 42000, "Second Wave", col="#FF0000")

text(x = as.Date("2021-03-16"), y = 28000, "Third Wave", col="#FF0000")
abline(h = 27354, col="#FF0000", lwd=2)

Calendar Heatmap

<<EOS
<div id="heatmap-total"></div>
<script>
calendar_heatmap.create({
    data: [
    #{data.map { |x| "{ day: \"%s\", count: %d }" % [x[0], x[1].to_i] }.join(",") }
    ],
    date_var: "day",
    fill_var: "count",
    target: "#heatmap-total"
});
</script>
EOS

New Cases by Region

Tabular Data

The table is built as follows:

egrep selects entries in the current month, getting the current month from the date command
the first sed gets rid of the “T17:00:00” timestamp in dates
the second sed compacts the date, by removing the year and replacing the month number with its name
datamash pivots the table by region and date

current_month=$(date +"%b")
current_month_no=$(date +"%m")
year=$(date +"%Y")

egrep ${year}-${current_month_no} ./data/dpc-covid19-ita-regioni.csv | sed -e "s/T1[78]:00:00//g" | sed -e "s/${year}-${current_month_no}-/${current_month} /g" | datamash -t, crosstab 4,1 sum 13

	Apr 01	Apr 02	Apr 03	Apr 04	Apr 05	Apr 06	Apr 07	Apr 08	Apr 09	Apr 10	Apr 11
Abruzzo	2301	2068	1800	762	2900	1982	2101	2079	2122	1649	744
Basilicata	1061	935	625	505	1094	712	822	731	701	538	350
Calabria	3477	2502	1692	1997	3641	2342	2326	2173	2100	1619	1431
Campania	7903	7537	6373	3384	10099	7277	7435	7224	6795	4302	2862
Emilia-Romagna	4941	4600	4363	3195	3103	5343	4942	5136	4717	4565	2909
Friuli Venezia Giulia	1417	1169	712	282	1562	1133	1223	1141	1206	808	210
Lazio	8460	9115	6533	3834	9903	7782	7591	6849	7255	6415	3780
Liguria	1507	1576	1011	523	1979	1518	1537	1481	1490	1194	569
Lombardia	9053	8782	6371	2512	11666	9094	9368	8681	8540	6611	2560
Marche	2386	2180	1885	860	2814	1981	2249	2086	1885	1789	714
Molise	318	515	389	166	378	430	464	370	180	422	181
P.A. Bolzano	573	493	357	242	847	512	514	552	443	259	145
P.A. Trento	383	401	297	163	536	466	489	473	439	353	145
Piemonte	3109	2665	1724	2566	3895	3368	3330	3252	3020	1758	2832
Puglia	6872	6670	4929	2683	8441	5773	5578	5352	5478	4137	1859
Sardegna	2008	1677	1064	760	2631	1989	1780	1848	1462	1207	1164
Sicilia	4749	4952	3435	1993	5769	4566	4142	4448	4005	3548	1818
Toscana	4793	4698	3882	1640	5974	4458	4751	4395	4134	3653	1606
Umbria	1639	1383	1203	500	1745	1473	1270	1267	1138	1130	621
Valle d’Aosta	67	64	62	15	116	90	79	69	62	78	21
Veneto	7333	6821	4881	2048	9080	6989	7605	6928	6820	5341	1847

Line Plots

# how many rows and columns?
par(mfrow=c(11, 2))
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, 
          max=max(data$nuovi_positivi),
          filter=TRUE, 
          textlabels=FALSE,
          variables=c("nuovi_positivi"),
          graphtypes=c("l"),
          colors=c("black"))
}

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.

Footnotes:

¹

The CSV files containing the data are updated by the first code block in index.html.