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 last updated on <2021-07-17 Sat>.

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

We read the data from the CSV files of the Civil Protection repository:

PATH="./data"

# 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)
abline(v = as.Date("2021-03-01"), col="#FF0000", lwd=2)

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

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

	Jul 01	Jul 02	Jul 03	Jul 04	Jul 05	Jul 06	Jul 07	Jul 08	Jul 09	Jul 10	Jul 11	Jul 12	Jul 13	Jul 14	Jul 15	Jul 16	Jul 17
Abruzzo	50	23	28	25	7	33	35	49	32	34	25	0	33	42	50	25	34
Basilicata	16	4	11	6	1	7	12	15	7	9	5	0	3	10	5	10	11
Calabria	27	13	56	24	11	26	38	54	48	16	54	13	45	56	32	77	51
Campania	107	98	139	110	68	108	208	162	226	210	169	69	136	188	234	204	238
Emilia-Romagna	61	46	65	52	58	51	62	94	99	96	93	118	76	117	167	200	207
Friuli Venezia Giulia	15	21	10	9	2	13	22	19	17	28	6	17	15	42	21	26	30
Lazio	72	64	85	83	83	58	104	112	135	176	164	172	166	208	353	443	500
Liguria	12	11	7	13	8	7	20	16	17	26	26	9	25	40	31	54	55
Lombardia	136	143	140	131	51	129	100	215	230	205	250	95	241	420	381	399	438
Marche	46	28	32	41	4	39	52	42	19	35	39	9	21	60	40	52	53
Molise	1	0	1	3	0	0	4	4	10	0	7	1	0	16	10	23	9
P.A. Bolzano	14	4	7	4	1	15	6	5	12	12	11	0	18	13	22	10	34
P.A. Trento	5	7	3	6	0	1	4	4	3	7	6	2	10	24	23	33	26
Piemonte	20	25	35	40	15	38	31	42	40	33	47	17	53	84	68	115	55
Puglia	40	43	47	25	15	60	49	58	44	41	34	19	97	44	42	46	81
Sardegna	10	25	8	21	9	21	25	28	51	43	52	51	86	135	112	140	190
Sicilia	137	115	134	102	58	144	109	219	201	192	183	150	174	288	353	386	431
Toscana	53	58	50	49	41	59	41	94	79	63	87	66	65	90	173	169	222
Umbria	3	9	5	11	3	1	7	11	13	17	6	4	15	15	20	56	30
Valle d’Aosta	2	0	3	0	0	0	1	2	1	0	2	0	1	0	0	5	2
Veneto	55	57	66	53	45	97	80	149	106	157	125	76	254	261	318	425	424

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"))
}