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 <2020-12-28 Mon>.

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)
abline(h = new_national[27,]$nuovi_positivi, col="#FF0000", lwd=3)

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

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

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31
Jan
Feb																								221	93	78	250	238	240
Mar	566	342	466	587	769	778	1247	1492	1797	977	2313	2651	2547	3497	3590	3233	3526	4207	5322	5986	6557	5560	4789	5249	5210	6153	5959	5974	5217	4050	4053
Apr	4782	4668	4585	4805	4316	3599	3039	3836	4204	3951	4694	4092	3153	2972	2667	3786	3493	3491	3047	2256	2729	3370	2646	3021	2357	2324	1739	2091	2086	1872
May	1965	1900	1389	1221	1075	1444	1401	1327	1083	802	744	1402	888	992	789	875	675	451	813	665	642	652	669	531	300	397	584	593	516	416	355
Jun	178	318	321	177	518	270	197	280	283	202	379	163	346	338	303	210	329	333	251	262	224	218	122	190	296	259	175	174	126	142
Jul	187	201	223	235	192	208	138	193	229	276	188	234	169	114	163	230	233	249	219	190	129	282	306	252	275	255	170	212	289	386	379
Aug	295	239	159	190	384	402	552	347	463	259	412	481	523	574	629	479	320	403	642	845	947	1071	1210	953	878	1367	1411	1462	1444	1365	996
Sep	978	1326	1397	1733	1694	1297	1108	1370	1434	1597	1616	1501	1458	1008	1229	1452	1585	1907	1638	1587	1350	1392	1640	1786	1912	1869	1766	1494	1648	1851
Oct	2548	2499	2844	2578	2257	2677	3678	4458	5372	5724	5456	4619	5901	7332	8804	10010	10925	11705	9338	10874	15199	16079	19143	19644	21273	17012	21994	24991	26831	31084	31758
Nov	29907	22253	28244	30550	34505	37809	39811	32616	25271	35098	32961	37978	40902	37255	33979	27354	32191	34282	36176	37242	34767	28337	22930	23232	25853	29003	28352	26323	20648	16377
Dec	19350	20709	23225	24099	21052	18887	13720	14842	12756	16999	18727	19903	17938	12030	14844	17572	18236	17992	16308	15104	10872	13318	14522	18040	19037	10431	8913	8585	11224	16202	23477

New Cases by Region

Tabular Data

New cases by region in August.

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")

egrep 2020-${current_month_no} ./data/dpc-covid19-ita-regioni.csv | sed -e "s/T17:00:00//g" | sed -e "s/2020-${current_month_no}-/${current_month} /g" | datamash -t, crosstab 4,1 sum 13

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"))
}

Author: Adolfo Villafiorita

Last modified: 2020-12-28 Mon 08:45 (created on: 2020-08-01 Sat 00:00)

Published: 2021-01-13 Wed 18:50