COVID-19 Number of Tests in Italy

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Introduction

This page presents some data about the number of tests and people tested for COVID-19 over time in Italy and compares them with the number of people found positive.

This page was created on <2020-08-20 Thu> and last updated on <2022-04-12 Tue>.

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.

Getting data into R

We first read the data from the Civil Protection repository adding the ratio between positives and tests, computed on the same day and computed with data shifted by two days (on the assumption tests take two days to complete).

In fact data about tests is used with different semantics by different regions. Some regions reports tests with results (and the ratio new positives / tests makes sense). Other reports the number of test performed, in which case the correct ratio is between positives and tests performed some days earlier. We assume two days and report both ratios for all regions. See the following issue on GitHub for an explanation and some more details https://github.com/pcm-dpc/COVID-19/issues/577 (in Italian).

DIGITS = 4

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

national$nuovi_casi_testati = c(NA, diff(national$casi_testati, 1))
national$p_over_t <- round(national$nuovi_positivi / national$nuovi_casi_testati, digits = DIGITS) * 100

national$nuovi_tamponi = c(NA, diff(national$tamponi, 1))
national$p_tamponi_over_t <- round(national$nuovi_positivi / national$nuovi_tamponi, digits = DIGITS) * 100

# national$nuovi_casi_testati_2 <- c(NA, NA, head(national$nuovi_casi_testati, -2))
# national$p_over_t_2 = round(national$nuovi_positivi / national$nuovi_casi_testati_2, digits = DIGITS) * 100

# national$nuovi_tamponi_2 <- c(NA, NA, head(national$tamponi_2, -2))
# national$p_tamponi_over_t_2 = round(national$nuovi_positivi / national$nuovi_tamponi_2, digits = DIGITS) * 100

Concerning the regional level, computed columns, such as the number of people tested in a day, have to be computed after filtering, or the diif will work on values from different regions.

# evolution over time, by Region
data = read.csv(file.path(PATH, "dpc-covid19-ita-regioni.csv"))
data$data <- as.Date(data$data)

These are the columns we are interested in and their translation in English:

cols = c(
  "data",
  "nuovi_positivi",
  "nuovi_tamponi",
  "nuovi_casi_testati",
  "p_tamponi_over_t",
  "p_over_t"
)

We now define a function to ouput the last N rows of the input data frame. The real “challenge”, here, is transposing the data, to get a more natural presentation (with time progressing from left to right).

table_data <- function(df, cols, rows = 10) {
  # get the last 10 elements and the interesting columns of the dataframe
  f  <- tail(df, rows)
  rf <- f[, cols]

  # the labels in the transposed matrix are the column names of the original data.frame
  row_labels  <- colnames(rf)
  # the columns in the trasposed matrix are the dates
  col_labels  <- c("Label", format(rf$data, "%a, %b %d"))

  rft <- data.frame(row_labels, t(rf))
  colnames(rft) <- col_labels
  return(rft[-1,])
}

People Tested and Cases in Italy

Data of the last ten days

table_data(national, cols)
Label Sat, Apr 02 Sun, Apr 03 Mon, Apr 04 Tue, Apr 05 Wed, Apr 06 Thu, Apr 07 Fri, Apr 08 Sat, Apr 09 Sun, Apr 10 Mon, Apr 11
nuovi_positivi 70803 53588 30630 88173 69278 69596 66535 63992 51376 28368
nuovi_tamponi 477041 364182 1383218 588576 461448 469803 442029 438449 352265 192782
nuovi_casi_testati 90285 79757 220934 104557 95155 89483 89666 79232 73329 41812
p_tamponi_over_t 14.84 14.71 2.21 14.98 15.01 14.81 15.05 14.6 14.58 14.72
p_over_t 78.42 67.19 13.86 84.33 72.81 77.78 74.2 80.77 70.06 67.85

New Cases

New cases.

## add extra space to right margin of plot within frame
par(mar=c(5, 4, 4, 6) + 0.1)

## Allow a second plot on the same graph
# par(new=TRUE)
new_cases_limits = c( min(national[national$data >= "2020-08-01", c("nuovi_positivi")]), max(national[national$data >= "2020-08-01", c("nuovi_positivi")]) )

p = plot(x = national[national$data >= "2020-08-01", c("data")], 
     y = national[national$data >= "2020-08-01", c("nuovi_positivi")], 
     type="l", lwd=6, pch=21, cex=1.5, col=c("#AA0000"),
     axes=FALSE,
     ylim=new_cases_limits,
     ylab="", xlab="")
text(x = tail(national[national$data >= "2020-08-01", c("data")], 5),
     y = tail(national[national$data >= "2020-08-01", c("nuovi_positivi")], 5),
     labels = tail(national[national$data >= "2020-08-01", c("nuovi_positivi")], 5),
     pos = 1, cex = 1, col="#AA0000")
mtext("New Cases", side=4, line=4, col="#AA0000") 
axis(4, ylim=new_cases_limits, las=1)

grid(p, col = "black", lty = "dotted")

# x-axis
dates = national[national$data >= "2020-08-01", c("data")]
axis.Date(1, at=seq(min(dates), max(dates), by="week"), format="%b %d", las=2)
mtext("Day", side=1, line=2.5)

## Add Legend
legend("topleft", legend = c("Tests", "New Cases"),
       text.col = c("#3B3176", "#AA0000"), pch= c(15, 17), col=c("#3B3176", "#AA0000"))

new_cases_italia.png

New Cases Tested

plot(x = national[national$data >= "2020-08-01", c("data")], 
     y = national[national$data >= "2020-08-01", c("nuovi_casi_testati")], 
     type="l", lwd=6, pch=16, cex=2.5, col=c("#3B3176"))
text(x = tail(national[national$data >= "2020-08-01", c("data")], 1),
     y = tail(national[national$data >= "2020-08-01", c("nuovi_casi_testati")], 1),
     labels = tail(national[national$data >= "2020-08-01", c("nuovi_casi_testati")], 1),
     pos = 4, cex = 1.2, col=c("#3B3176"))
 grid(col="black")

tests_italia.png

Number of Tests and New Cases Tested

Plot new cases and tests together. (Solution taken from How can I plot with 2 different y-axes? on Stack Overflow.)

## add extra space to right margin of plot within frame
par(mar=c(5, 4, 4, 6) + 0.1)

## Plot first set of data and draw its axis
tests_limits = c( min(national[national$data >= "2020-08-01", c("nuovi_casi_testati")]), max(national[national$data >= "2020-08-01", c("nuovi_casi_testati")]) )
plot(x = national[national$data >= "2020-08-01", c("data")], 
     y = national[national$data >= "2020-08-01", c("nuovi_casi_testati")], 
     type="l", lwd=6, pch=11, cex=1.5, col=c("#3B3176"),
     axes=FALSE,
     ylim=tests_limits,
     ylab="", xlab="")
text(x = tail(national[national$data >= "2020-08-01", c("data")], 1),
     y = tail(national[national$data >= "2020-08-01", c("nuovi_casi_testati")], 1),
     labels = tail(national[national$data >= "2020-08-01", c("nuovi_casi_testati")], 1),
     pos = 4, cex = 1, col=c("#3B3176"))
mtext("Number of Tests", side=2, col="#3B3176", line=4) 
axis(2, ylim=tests_limits, col="black", las=1)  
box()

## Allow a second plot on the same graph
par(new=TRUE)
new_cases_limits = c( min(national[national$data >= "2020-08-01", c("nuovi_positivi")]), max(national[national$data >= "2020-08-01", c("nuovi_positivi")]) )

p = plot(x = national[national$data >= "2020-08-01", c("data")], 
     y = national[national$data >= "2020-08-01", c("nuovi_positivi")], 
     type="l", lwd=6, pch=21, cex=1.5, col=c("#AA0000"),
     axes=FALSE,
     ylim=new_cases_limits,
     ylab="", xlab="")
text(x = tail(national[national$data >= "2020-08-01", c("data")], 1),
     y = tail(national[national$data >= "2020-08-01", c("nuovi_positivi")], 1),
     labels = tail(national[national$data >= "2020-08-01", c("nuovi_positivi")], 1),
     pos = 4, cex = 1, col="#AA0000")
mtext("New Cases", side=4, line=4, col="#AA0000") 
axis(4, ylim=new_cases_limits, las=1)

grid(p, col = "black", lty = "dotted")

# x-axis
dates = national[national$data >= "2020-08-01", c("data")]
axis.Date(1, at=seq(min(dates), max(dates), by="week"), format="%b %d", las=2)
mtext("Day", side=1, line=2.5)

## Add Legend
legend("topleft", legend = c("Tests", "New Cases"),
       text.col = c("#3B3176", "#AA0000"), pch= c(15, 17), col=c("#3B3176", "#AA0000"))

tests_and_new_cases_italia.png

Positive/Number of Tests

Here we plot the number of positive people over tests performed. The standard measurement is the ratio between positive and tests performed (shown in blue). The way I understand it is that this number also includes tests performed on people already diagnosed and recovered.

The second graph, in red, shows the ration of positive over new people tested, that is, of all the people not yet diagnosed, how many resulted positive?

plot(national$p_over_t ~ national$data, type="o", lwd=3, pch=21, col="#ff0000", main="Positive over Tests", xlab="Date", ylab="Percentage")
text(y = tail(national, 1)$p_over_t, x = tail(national, 1)$data, lab = paste(tail(national, 1)$p_over_t, "%", sep=""), pos=4, col="#ff0000", cex=1.3)

# Second plot with Positive over tests
p = lines(national$p_tamponi_over_t ~ national$data, type="o", lwd=3, pch=21, col="#000088", xlab="Date", ylab="Percentage")
text(y = tail(national, 1)$p_tamponi_over_t, x = tail(national, 1)$data, lab = paste(tail(national, 1)$p_tamponi_over_t, "%", sep=""), pos=4, col="#000088", cex=1.3)

## Add Legend
grid(col="black")
legend("bottomleft", legend = c("Positive over new People Tested", "Positive over Tests Performed"),
       text.col = c("#ff0000", "#000088"), pch= c(15, 17), col=c("#AA0000", "#000088"))

positive_over_tests_italia.png

People Tested and Cases in Trentino

region <- subset(data, denominazione_regione == "P.A. Trento")

region$nuovi_casi_testati = c(NA, diff(region$casi_testati, 1))

region$p_over_t <- round(region$nuovi_positivi / region$nuovi_casi_testati, digits = DIGITS) * 100
region$nuovi_casi_testati_2 = c(NA, NA, diff(region$casi_testati, 2))
region$p_over_t_2 = round(region$nuovi_positivi / region$nuovi_casi_testati_2, digits = DIGITS) * 100
region$nuovi_casi_testati_2 <- c(NA, NA, head(region$nuovi_casi_testati, -2))
region$p_over_t_2 = round(region$nuovi_positivi / region$nuovi_casi_testati_2, digits = DIGITS) * 100

region$nuovi_tamponi = c(NA, diff(region$tamponi, 1))
region$p_tamponi_over_t <- round(region$nuovi_positivi / region$nuovi_tamponi, digits = DIGITS) * 100
region$nuovi_tamponi_2 <- c(NA, NA, head(region$tamponi_2, -2))
region$p_tamponi_over_t_2 = round(region$nuovi_positivi / region$nuovi_tamponi_2, digits = DIGITS) * 100

table_data(region, cols)
Label Sat, Apr 02 Sun, Apr 03 Mon, Apr 04 Tue, Apr 05 Wed, Apr 06 Thu, Apr 07 Fri, Apr 08 Sat, Apr 09 Sun, Apr 10 Mon, Apr 11
nuovi_positivi 401 297 163 536 466 489 473 439 353 145
nuovi_tamponi 3888 2764 1669 5217 3815 4180 3726 3655 2795 1563
nuovi_casi_testati 479 304 100 449 446 398 362 420 273 99
p_tamponi_over_t 10.31 10.75 9.77 10.27 12.21 11.7 12.69 12.01 12.63 9.28
p_over_t 83.72 97.7 163.0 119.38 104.48 122.86 130.66 104.52 129.3 146.46

People Tested and Cases in Liguria

region <- subset(data, denominazione_regione == "Liguria")

region$nuovi_casi_testati = c(NA, diff(region$casi_testati, 1))

region$p_over_t <- round(region$nuovi_positivi / region$nuovi_casi_testati, digits = DIGITS) * 100
region$nuovi_casi_testati_2 = c(NA, NA, diff(region$casi_testati, 2))

region$nuovi_tamponi = c(NA, diff(region$tamponi, 1))
region$p_tamponi_over_t <- round(region$nuovi_positivi / region$nuovi_tamponi, digits = DIGITS) * 100

table_data(region, cols)
Label Sat, Apr 02 Sun, Apr 03 Mon, Apr 04 Tue, Apr 05 Wed, Apr 06 Thu, Apr 07 Fri, Apr 08 Sat, Apr 09 Sun, Apr 10 Mon, Apr 11
nuovi_positivi 1576 1011 523 1979 1518 1537 1481 1490 1194 569
nuovi_tamponi 10732 6981 3554 14258 10455 10874 10086 10266 6907 3224
nuovi_casi_testati 1246 933 474 1666 1360 1324 1185 1220 949 589
p_tamponi_over_t 14.69 14.48 14.72 13.88 14.52 14.13 14.68 14.51 17.29 17.65
p_over_t 126.48 108.36 110.34 118.79 111.62 116.09 124.98 122.13 125.82 96.6

People Tested and Cases in Veneto

region <- subset(data, denominazione_regione == "Veneto")

region$nuovi_casi_testati = c(NA, diff(region$casi_testati, 1))
region$p_over_t <- round(region$nuovi_positivi / region$nuovi_casi_testati, digits = DIGITS) * 100

region$nuovi_tamponi = c(NA, diff(region$tamponi, 1))
region$p_tamponi_over_t <- round(region$nuovi_positivi / region$nuovi_tamponi, digits = DIGITS) * 100

table_data(region, cols)
Label Sat, Apr 02 Sun, Apr 03 Mon, Apr 04 Tue, Apr 05 Wed, Apr 06 Thu, Apr 07 Fri, Apr 08 Sat, Apr 09 Sun, Apr 10 Mon, Apr 11
nuovi_positivi 6821 4881 2048 9080 6989 7605 6928 6820 5341 1847
nuovi_tamponi 63855 42701 16666 83925 63883 67587 60290 63707 41447 16600
nuovi_casi_testati 3892 2974 1155 4973 3776 4181 3610 3684 3020 1463
p_tamponi_over_t 10.68 11.43 12.29 10.82 10.94 11.25 11.49 10.71 12.89 11.13
p_over_t 175.26 164.12 177.32 182.59 185.09 181.89 191.91 185.12 176.85 126.25

People Tested and Cases in Lombardia

region <- subset(data, denominazione_regione == "Lombardia")

region$nuovi_casi_testati = c(NA, diff(region$casi_testati, 1))
region$p_over_t <- round(region$nuovi_positivi / region$nuovi_casi_testati, digits = DIGITS) * 100

region$nuovi_tamponi = c(NA, diff(region$tamponi, 1))
region$p_tamponi_over_t <- round(region$nuovi_positivi / region$nuovi_tamponi, digits = DIGITS) * 100

table_data(region, cols)
Label Sat, Apr 02 Sun, Apr 03 Mon, Apr 04 Tue, Apr 05 Wed, Apr 06 Thu, Apr 07 Fri, Apr 08 Sat, Apr 09 Sun, Apr 10 Mon, Apr 11
nuovi_positivi 8782 6371 2512 11666 9094 9368 8681 8540 6611 2560
nuovi_tamponi 66588 52432 24242 93356 68012 72825 63503 65169 51773 22306
nuovi_casi_testati 7122 5573 2531 9132 7375 7301 6393 6715 5366 2179
p_tamponi_over_t 13.19 12.15 10.36 12.5 13.37 12.86 13.67 13.1 12.77 11.48
p_over_t 123.31 114.32 99.25 127.75 123.31 128.31 135.79 127.18 123.2 117.49