New Cases in Italy

Table of Contents

Introduction
R Functions
New Cases in Italy
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 <2021-03-21 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

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

Tabular Data

This section used to present cases in a grid Month/Day… however, I should now update it with Month-Year/Day, since the pandemia has been around for more than a year, now.

require 'date'

def find_new_cases_for date, table
  table.each do |row|
    return row[1] if date == Date.parse(row[0])
  end
  return ""
end

year_grid = Array.new
legenda = (0..31).map { |x| x }
legenda[0] = ""
year_grid << legenda

(1..12).each do |month|
  month_row = Array.new(32)
  month_row[0] = Date.new(2020, month, 1).strftime("%b")
  (1..31).each do |day|
    if Date.valid_date?(2020, month, day) 
      date = Date.new(2020, month, day)
      cases = find_new_cases_for(date, table)
      month_row[day] = cases
    else
      month_row[day] = ""
    end
  end
  year_grid << month_row
end
year_grid

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

	Mar 01	Mar 02	Mar 03	Mar 04	Mar 05	Mar 06	Mar 07	Mar 08	Mar 09	Mar 10	Mar 11	Mar 12	Mar 13	Mar 14	Mar 15	Mar 16	Mar 17	Mar 18	Mar 19	Mar 20	Mar 21
Abruzzo	199	246	573	552	473	442	553	273	187	372	608	515	320	323	329	212	304	462	417	370	353
Basilicata	75	147	124	122	164	178	144	28	182	115	151	141	137	106	67	166	126	179	116	147	108
Calabria	151	102	230	226	260	293	228	89	149	283	414	305	275	300	213	311	216	404	376	394	412
Campania	1896	2046	2635	2780	2842	2843	2560	1644	2709	3034	2981	2644	2940	2449	1823	2656	2665	2223	1997	2196	1810
Emilia-Romagna	2597	2040	2456	2545	3246	3232	3056	2987	2429	2155	2845	3477	2950	3023	2822	2184	2026	2531	3188	2560	2448
Friuli Venezia Giulia	172	479	693	643	823	661	466	415	598	866	991	971	953	604	398	672	986	1058	910	906	578
Lazio	1044	1188	1520	1702	1525	1563	1399	1175	1431	1654	1800	1757	1998	1812	1536	1497	1728	1963	2188	1821	1793
Liguria	309	337	368	414	338	272	384	215	248	342	405	406	307	321	242	343	321	324	427	458	380
Lombardia	2135	3762	4590	5174	5210	5658	4397	2301	4084	4422	5849	6262	5809	4334	2185	4235	4490	5641	5518	4810	4003
Marche	446	415	759	919	1027	836	852	349	423	881	921	874	1000	616	415	529	856	820	822	856	700
Molise	41	18	116	56	137	49	67	35	24	80	84	73	104	52	17	18	77	81	41	62	57
P.A. Bolzano	121	236	280	253	230	218	182	88	178	213	203	147	183	103	13	175	158	165	127	116	76
P.A. Trento	57	220	357	229	406	324	275	83	213	405	370	281	239	273	104	190	285	256	306	307	155
Piemonte	1155	1609	1537	2167	2283	1793	1543	1214	2018	2086	2322	2929	2159	1530	1742	2074	2374	2357	2997	2141	1751
Puglia	631	1021	1261	1438	1418	1483	1155	594	1286	1571	1634	1774	1700	1542	715	1126	1734	2082	1785	1983	1546
Sardegna	45	70	101	81	89	73	95	68	146	65	128	106	103	118	74	79	92	121	134	102	176
Sicilia	478	566	539	560	519	592	576	515	595	695	672	679	650	613	523	598	782	789	859	782	699
Toscana	877	1058	1163	1239	1231	1293	1355	1000	1001	1293	1302	1304	1326	1305	1106	1247	1275	1513	1365	1510	1358
Umbria	79	289	301	267	295	283	242	42	232	296	283	219	196	270	66	159	324	168	182	206	142
Valle d’Aosta	3	6	9	11	15	16	7	30	8	20	33	28	31	34	36	24	49	37	63	61	28
Veneto	603	1228	1272	1487	1505	1539	1229	757	1608	1561	1677	1932	2682	1587	841	1901	2191	1761	1917	2044	1586

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"))
}