Epidemiology of IMD (from Wikipedia)

It is spread through saliva and other respiratory secretions during coughing, sneezing, kissing, and chewing on toys. Inhalation of respiratory droplets from a carrier which may be someone who is themselves in the early stages of disease can transmit the bacteria. […] The incubation period is short, from 2 to 10 days.

Disclaimer (formally required by the ECDC) The views and opinions of the authors expressed herein do not necessarily state or reflect those of the ECDC. The accuracy of the authors’ statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data and for data management, data merging and data collation after provision of the data. ECDC shall not be held liable for improper or incorrect use of the data.
Caveat (about monthly counts) The sole purpose of this exercise is to illustrate data import and the "sts" class. Note that monthly counts are generally inconvenient for epidemic models (or timely monitoring, for that matter) of IMD cases or common respiratory infections: a single time interval would typically “hide” several generations of infections.

Import raw data (code provided)

We need to prepare the main ingredients of a spatial "sts" object:

  1. observed time series of counts (by region)

  2. population numbers to calculate incidence values

  3. map of the regions for visualizations

The bold words correspond to arguments of the sts() constructor function.

observed time series of counts

The counts downloaded from the ECDC atlas cover the years 1999–2018 and are provided in a (gzip-compressed) CSV file. This is how the first 3 lines look like:

"HealthTopic","Population","Indicator","Unit","Time","RegionCode","RegionName","NumValue","TxtValue"
"Invasive meningococcal disease","Confirmed cases","Reported cases","N","1999-01","AT","Austria",11.000000000,""
"Invasive meningococcal disease","Confirmed cases","Reported cases","N","1999-01","BE","Belgium",24.000000000,""

The counts (NumValue) are given in the long format where each row corresponds to one time/region combination. We import the counts from the CSV file and reshape the data into the wide (matrix) format with one column per region. This is the format typically used for multivariate time series, including in sts() and R’s basic ts() function.

ecdc_long <- read.csv(
    file = "data/ECDC_surveillance_data_IMD.csv.gz",  # R can read compressed files
    na.strings = "-")  # always important to know how NA's are encoded!
head(ecdc_long, 10)
                      HealthTopic      Population      Indicator Unit    Time RegionCode
1  Invasive meningococcal disease Confirmed cases Reported cases    N 1999-01         AT
2  Invasive meningococcal disease Confirmed cases Reported cases    N 1999-01         BE
3  Invasive meningococcal disease Confirmed cases Reported cases    N 1999-01         CZ
4  Invasive meningococcal disease Confirmed cases Reported cases    N 1999-01         DE
5  Invasive meningococcal disease Confirmed cases Reported cases    N 1999-01         DK
6  Invasive meningococcal disease Confirmed cases Reported cases    N 1999-01         EE
7  Invasive meningococcal disease Confirmed cases Reported cases    N 1999-01         EL
8  Invasive meningococcal disease Confirmed cases Reported cases    N 1999-01         ES
9  Invasive meningococcal disease Confirmed cases Reported cases    N 1999-01   EU_EEA31
10 Invasive meningococcal disease Confirmed cases Reported cases    N 1999-01         FI
   RegionName NumValue TxtValue
1     Austria       11       NA
2     Belgium       24       NA
3     Czechia        5       NA
4     Germany       37       NA
5     Denmark       19       NA
6     Estonia        0       NA
7      Greece       18       NA
8       Spain      172       NA
9      EU/EEA     1020       NA
10    Finland        6       NA
## exclude aggregate counts
ecdc_long <- subset(ecdc_long, !startsWith(RegionName, "EU"))

## reshape from long to wide format of multivariate time series
ecdc <- reshape(ecdc_long[c("Time", "RegionCode", "NumValue")],
                direction = "wide", idvar = "Time", timevar = "RegionCode")
names(ecdc) <- sub("NumValue.", "", names(ecdc), fixed = TRUE)
row.names(ecdc) <- ecdc$Time; ecdc$Time <- NULL
head(ecdc)
        AT BE CZ DE DK EE EL  ES FI FR IE IS IT LU MT NL NO PL SE SI  UK LT PT LV HU SK CY BG RO
1999-01 11 24  5 37 19  0 18 172  6 43 53  2 36  0  0 81  4 11  2  1 495 NA NA NA NA NA NA NA NA
1999-02 14 37  8 48 17  0 16 123  9 65 43  2 31  0  2 68 11  9  3  0 267 NA NA NA NA NA NA NA NA
1999-03 12 49 11 54 28  0 42  90  5 35 53  1 29  0  3 82 10  8  6  0 224 NA NA NA NA NA NA NA NA
1999-04  6 23  9 36 22  1 21  70  3 30 40  2 23  0  0 33  4  4  2  1 203 NA NA NA NA NA NA NA NA
1999-05  3 27 14 36 11  0 16  78  7 18 34  2 18  0  1 41  6  6  3  1 140 NA NA NA NA NA NA NA NA
1999-06  4 25 10 30 12  0 11  50  8 19 24  2 21  0  1 38  4  1  2  0 199 NA NA NA NA NA NA NA NA
        HR
1999-01 NA
1999-02 NA
1999-03 NA
1999-04 NA
1999-05 NA
1999-06 NA
## restrict to countries with complete data (KISS)
ecdc <- ecdc[, colSums(is.na(ecdc)) == 0]
tail(ecdc)
        AT CZ DE DK EE EL ES FI FR IE IS IT NL NO PL SI UK
2018-07  1  8 18  5  1  2 25  1 34  5  0 12 16  2  7  1 28
2018-08  4  7 15  1  0  3 13  1 17  5  0  8 13  1  8  0 32
2018-09  1  0 14  0  0  1 22  2 17  7  0  7  7  1  4  2 44
2018-10  2  3 27  4  0  2 32  3 30  4  0 12 21  3 18  2 57
2018-11  3  7 15  3  0  4 26  0 28  4  0 10 10  0 21  2 44
2018-12  1  7 17  3  0  4 34  1 47  6  0 10 13  2 21  3 60

Country codes are given in the Eurostat Glossary.

population data

Population counts are needed to calculate incidence values. Eurostat provides these as dataset tps00001. We have downloaded the values for 2018 as a CSV file.

popdata <- read.csv("data/Eurostat_population_2018.csv")
head(popdata, 3)
             DATAFLOW       LAST.UPDATE freq indic_de geo TIME_PERIOD OBS_VALUE OBS_FLAG
1 ESTAT:TPS00001(1.0) 17/03/22 23:00:00    A      JAN  AD        2018     74794        e
2 ESTAT:TPS00001(1.0) 17/03/22 23:00:00    A      JAN  AL        2018   2870324         
3 ESTAT:TPS00001(1.0) 17/03/22 23:00:00    A      JAN  AM        2018   2972732         
stopifnot(colnames(ecdc) %in% popdata$geo)
pop <- setNames(popdata$OBS_VALUE, popdata$geo)[colnames(ecdc)]
pop
      AT       CZ       DE       DK       EE       EL       ES       FI       FR       IE       IS 
 8822267 10610055 82792351  5781190  1319133 10741165 46658447  5513130 67026224  4830392   348450 
      IT       NL       NO       PL       SI       UK 
60483973 17181084  5295619 37976687  2066880 66273576

map

The "sts" class can be used without a map, but incorporating one enables a spatial view of the data. We retrieve a suitable GeoJSON dataset of administrative boundaries from Eurostat/GISCO, with Copyright (C) EuroGeographics. The result of importing that dataset is already provided in an RData file (so as to avoid potential problems with system requirements of the sf package):

load("data/map.RData", verbose = TRUE)
Loading objects:
  map
If you wish, see here how this map was imported 
library("sf")
## read NUTS1-level data from Eurostat/GISCO
map1 <- st_read("https://gisco-services.ec.europa.eu/distribution/v2/nuts/geojson/NUTS_RG_60M_2021_4326_LEVL_1.geojson")
## omit French overseas regions for a more compact map
map1 <- subset(map1, NUTS_ID != "FRY")
## union polygons by country, dropping data fields
map0 <- aggregate(map1[0], by = list(COUNTRY = map1$CNTR_CODE), FUN = sum)
## check that the map contains all country codes of `ecdc`
stopifnot(colnames(ecdc) %in% map0$COUNTRY)
row.names(map0) <- map0$COUNTRY  # used to match against colnames(observed)
## convert to "SpatialPolgons" for sts() [redundant in surveillance >= 1.23.1]
library("sp")
map <- geometry(as_Spatial(map0))
save(map, file = "data/map.RData", compress = "xz")
library("sp")
par(mar = c(0,0,0,0)) # no margin
plot(map)

Create an "sts" object (DIY)

Using the above ingredients (ecdc, pop, map) and knowing that the monthly ecdc time series starts at

(start <- as.numeric(strsplit(rownames(ecdc), "-")[[1]]))
[1] 1999    1

you can now create an "sts" object via the constructor function sts().

library("surveillance")
Lade nötiges Paket: xtable
This is surveillance 1.23.1; see 'package?surveillance' or
https://surveillance.R-Forge.R-project.org/ for an overview.
## See help("sts") and learn about the function arguments:
str(sts)
## Complete the following expression:
## IMD <- sts(....)
Solution 
IMD <- sts(ecdc, start = start, frequency = 12, # monthly data
           population = pop, map = map)

It should give the following printed output:

IMD
-- An object of class sts -- 
freq:        12 
start:       1999 1 
dim(observed):   240 17 

Head of observed:
     AT CZ DE DK EE EL  ES FI FR IE IS IT NL NO PL SI  UK
[1,] 11  5 37 19  0 18 172  6 43 53  2 36 81  4 11  1 495

map: 37 Polygons, without data

Try some methods

Try some of the methods for "sts" objects with the IMD dataset, see Section “Methods” in help(sts).

Extraction

Try:

  • dim(), colnames()

  • epoch(), epochInYear(), year()

  • observed(), population()

Solution 
dim(IMD)  # months x countries
[1] 240  17
colnames(IMD)
 [1] "AT" "CZ" "DE" "DK" "EE" "EL" "ES" "FI" "FR" "IE" "IS" "IT" "NL" "NO" "PL" "SI" "UK"
epoch(IMD, as.Date = TRUE)
  [1] "1999-01-01" "1999-02-01" "1999-03-01" "1999-04-01" "1999-05-01" "1999-06-01" "1999-07-01"
  [8] "1999-08-01" "1999-09-01" "1999-10-01" "1999-11-01" "1999-12-01" "2000-01-01" "2000-02-01"
 [15] "2000-03-01" "2000-04-01" "2000-05-01" "2000-06-01" "2000-07-01" "2000-08-01" "2000-09-01"
 [22] "2000-10-01" "2000-11-01" "2000-12-01" "2001-01-01" "2001-02-01" "2001-03-01" "2001-04-01"
 [29] "2001-05-01" "2001-06-01" "2001-07-01" "2001-08-01" "2001-09-01" "2001-10-01" "2001-11-01"
 [36] "2001-12-01" "2002-01-01" "2002-02-01" "2002-03-01" "2002-04-01" "2002-05-01" "2002-06-01"
 [43] "2002-07-01" "2002-08-01" "2002-09-01" "2002-10-01" "2002-11-01" "2002-12-01" "2003-01-01"
 [50] "2003-02-01" "2003-03-01" "2003-04-01" "2003-05-01" "2003-06-01" "2003-07-01" "2003-08-01"
 [57] "2003-09-01" "2003-10-01" "2003-11-01" "2003-12-01" "2004-01-01" "2004-02-01" "2004-03-01"
 [64] "2004-04-01" "2004-05-01" "2004-06-01" "2004-07-01" "2004-08-01" "2004-09-01" "2004-10-01"
 [71] "2004-11-01" "2004-12-01" "2005-01-01" "2005-02-01" "2005-03-01" "2005-04-01" "2005-05-01"
 [78] "2005-06-01" "2005-07-01" "2005-08-01" "2005-09-01" "2005-10-01" "2005-11-01" "2005-12-01"
 [85] "2006-01-01" "2006-02-01" "2006-03-01" "2006-04-01" "2006-05-01" "2006-06-01" "2006-07-01"
 [92] "2006-08-01" "2006-09-01" "2006-10-01" "2006-11-01" "2006-12-01" "2007-01-01" "2007-02-01"
 [99] "2007-03-01" "2007-04-01" "2007-05-01" "2007-06-01" "2007-07-01" "2007-08-01" "2007-09-01"
[106] "2007-10-01" "2007-11-01" "2007-12-01" "2008-01-01" "2008-02-01" "2008-03-01" "2008-04-01"
[113] "2008-05-01" "2008-06-01" "2008-07-01" "2008-08-01" "2008-09-01" "2008-10-01" "2008-11-01"
[120] "2008-12-01" "2009-01-01" "2009-02-01" "2009-03-01" "2009-04-01" "2009-05-01" "2009-06-01"
[127] "2009-07-01" "2009-08-01" "2009-09-01" "2009-10-01" "2009-11-01" "2009-12-01" "2010-01-01"
[134] "2010-02-01" "2010-03-01" "2010-04-01" "2010-05-01" "2010-06-01" "2010-07-01" "2010-08-01"
[141] "2010-09-01" "2010-10-01" "2010-11-01" "2010-12-01" "2011-01-01" "2011-02-01" "2011-03-01"
[148] "2011-04-01" "2011-05-01" "2011-06-01" "2011-07-01" "2011-08-01" "2011-09-01" "2011-10-01"
[155] "2011-11-01" "2011-12-01" "2012-01-01" "2012-02-01" "2012-03-01" "2012-04-01" "2012-05-01"
[162] "2012-06-01" "2012-07-01" "2012-08-01" "2012-09-01" "2012-10-01" "2012-11-01" "2012-12-01"
[169] "2013-01-01" "2013-02-01" "2013-03-01" "2013-04-01" "2013-05-01" "2013-06-01" "2013-07-01"
[176] "2013-08-01" "2013-09-01" "2013-10-01" "2013-11-01" "2013-12-01" "2014-01-01" "2014-02-01"
[183] "2014-03-01" "2014-04-01" "2014-05-01" "2014-06-01" "2014-07-01" "2014-08-01" "2014-09-01"
[190] "2014-10-01" "2014-11-01" "2014-12-01" "2015-01-01" "2015-02-01" "2015-03-01" "2015-04-01"
[197] "2015-05-01" "2015-06-01" "2015-07-01" "2015-08-01" "2015-09-01" "2015-10-01" "2015-11-01"
[204] "2015-12-01" "2016-01-01" "2016-02-01" "2016-03-01" "2016-04-01" "2016-05-01" "2016-06-01"
[211] "2016-07-01" "2016-08-01" "2016-09-01" "2016-10-01" "2016-11-01" "2016-12-01" "2017-01-01"
[218] "2017-02-01" "2017-03-01" "2017-04-01" "2017-05-01" "2017-06-01" "2017-07-01" "2017-08-01"
[225] "2017-09-01" "2017-10-01" "2017-11-01" "2017-12-01" "2018-01-01" "2018-02-01" "2018-03-01"
[232] "2018-04-01" "2018-05-01" "2018-06-01" "2018-07-01" "2018-08-01" "2018-09-01" "2018-10-01"
[239] "2018-11-01" "2018-12-01"
year(IMD)
  [1] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 2000 2000 2000 2000 2000 2000
 [19] 2000 2000 2000 2000 2000 2000 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
 [37] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2003 2003 2003 2003 2003 2003
 [55] 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004
 [73] 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2006 2006 2006 2006 2006 2006
 [91] 2006 2006 2006 2006 2006 2006 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007
[109] 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2009 2009 2009 2009 2009 2009
[127] 2009 2009 2009 2009 2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010
[145] 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2012 2012 2012 2012 2012 2012
[163] 2012 2012 2012 2012 2012 2012 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
[181] 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2015 2015 2015 2015 2015 2015
[199] 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
[217] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018
[235] 2018 2018 2018 2018 2018 2018
epochInYear(IMD)
  [1]  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7
 [32]  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2
 [63]  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9
 [94] 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4
[125]  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11
[156] 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6
[187]  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1
[218]  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12
observed(IMD) |> head()
     AT CZ DE DK EE EL  ES FI FR IE IS IT NL NO PL SI  UK
[1,] 11  5 37 19  0 18 172  6 43 53  2 36 81  4 11  1 495
[2,] 14  8 48 17  0 16 123  9 65 43  2 31 68 11  9  0 267
[3,] 12 11 54 28  0 42  90  5 35 53  1 29 82 10  8  0 224
[4,]  6  9 36 22  1 21  70  3 30 40  2 23 33  4  4  1 203
[5,]  3 14 36 11  0 16  78  7 18 34  2 18 41  6  6  1 140
[6,]  4 10 30 12  0 11  50  8 19 24  2 21 38  4  1  0 199
population(IMD) |> head()  # same dim() as 'observed' (could vary over time)
          AT       CZ       DE      DK      EE       EL       ES      FI       FR      IE     IS
[1,] 8822267 10610055 82792351 5781190 1319133 10741165 46658447 5513130 67026224 4830392 348450
[2,] 8822267 10610055 82792351 5781190 1319133 10741165 46658447 5513130 67026224 4830392 348450
[3,] 8822267 10610055 82792351 5781190 1319133 10741165 46658447 5513130 67026224 4830392 348450
[4,] 8822267 10610055 82792351 5781190 1319133 10741165 46658447 5513130 67026224 4830392 348450
[5,] 8822267 10610055 82792351 5781190 1319133 10741165 46658447 5513130 67026224 4830392 348450
[6,] 8822267 10610055 82792351 5781190 1319133 10741165 46658447 5513130 67026224 4830392 348450
           IT       NL      NO       PL      SI       UK
[1,] 60483973 17181084 5295619 37976687 2066880 66273576
[2,] 60483973 17181084 5295619 37976687 2066880 66273576
[3,] 60483973 17181084 5295619 37976687 2066880 66273576
[4,] 60483973 17181084 5295619 37976687 2066880 66273576
[5,] 60483973 17181084 5295619 37976687 2066880 66273576
[6,] 60483973 17181084 5295619 37976687 2066880 66273576
## note that "sts" objects have some more slots (used by monitoring algorithms)
showClass("sts")
Class "sts" [package "surveillance"]

Slots:
                                                                                      
Name:            epoch            freq           start        observed           state
Class:         numeric         numeric         numeric          matrix          matrix
                                                                                      
Name:            alarm      upperbound   neighbourhood  populationFrac             map
Class:          matrix          matrix          matrix          matrix SpatialPolygons
                                                      
Name:          control     epochAsDate   multinomialTS
Class:            list         logical         logical

Known Subclasses: "stsBP", "stsNC"

Subsetting

Try some ways of subsetting the multivariate time series using conventional indexing for matrix-like objects:

  • [i,j]
Solution 
## subsetting
IMD[1:3, "UK"] # a very short, univariate "sts"
-- An object of class sts -- 
freq:        12 
start:       1999 1 
dim(observed):   3 1 

Head of observed:
      UK
[1,] 495

map: 37 Polygons, without data
tail(IMD, 12)  # only the last 12 months
-- An object of class sts -- 
freq:        12 
start:       2018 1 
dim(observed):   12 17 

Head of observed:
     AT CZ DE DK EE EL ES FI FR IE IS IT NL NO PL SI  UK
[1,]  4  3 34  3  0  4 72  0 75 21  0 38 22  2 16  3 158

map: 37 Polygons, without data
IMD[year(IMD) > 2011,]
-- An object of class sts -- 
freq:        12 
start:       2012 1 
dim(observed):   84 17 

Head of observed:
     AT CZ DE DK EE EL ES FI FR IE IS IT NL NO PL SI  UK
[1,]  5  9 44  8  0  4 61  3 58 13  1 17 13  4 29  1 119

map: 37 Polygons, without data

To keep things simple, you may wish to restrict some of the following exercises to an (arbitrary) subset of 6 countries; let’s use those whose country code starts with one of the letters D, F, or N. Create this subset and name the object IMD6.

Solution 
IMD6 <- IMD[, grep("^[DFN]", colnames(IMD))]
IMD6
-- An object of class sts -- 
freq:        12 
start:       1999 1 
dim(observed):   240 6 

Head of observed:
     DE DK FI FR NL NO
[1,] 37 19  6 43 81  4

map: 37 Polygons, without data

Conversion

Try:

  • aggregate(), see ?aggregate.sts

  • as.data.frame(), possibly with argument tidy = TRUE (or tidy.sts())

  • as.ts()

Solution 
## collapse the multivariate time series in either dimension
## help(aggregate.sts)
aggregate(IMD6, by = "time")  # summing over time
-- An object of class sts -- 
freq:        240 
start:       1999 1 
dim(observed):   1 6 

Head of observed:
       DE   DK  FI    FR   NL  NO
[1,] 9184 1575 679 11250 5174 785

map: 37 Polygons, without data
aggregate(IMD6, by = "unit")  # overall time series
-- An object of class sts -- 
freq:        12 
start:       1999 1 
dim(observed):   240 1 

Head of observed:
     overall
[1,]     190
## convert to a data frame (omitting non-temporal slots)
as.data.frame(IMD6)              |> head(3)  # wide format
  observed.DE observed.DK observed.FI observed.FR observed.NL observed.NO epoch state.DE state.DK
1          37          19           6          43          81           4     1    FALSE    FALSE
2          48          17           9          65          68          11     2    FALSE    FALSE
3          54          28           5          35          82          10     3    FALSE    FALSE
  state.FI state.FR state.NL state.NO alarm.DE alarm.DK alarm.FI alarm.FR alarm.NL alarm.NO
1    FALSE    FALSE    FALSE    FALSE       NA       NA       NA       NA       NA       NA
2    FALSE    FALSE    FALSE    FALSE       NA       NA       NA       NA       NA       NA
3    FALSE    FALSE    FALSE    FALSE       NA       NA       NA       NA       NA       NA
  upperbound.DE upperbound.DK upperbound.FI upperbound.FR upperbound.NL upperbound.NO
1            NA            NA            NA            NA            NA            NA
2            NA            NA            NA            NA            NA            NA
3            NA            NA            NA            NA            NA            NA
  population.DE population.DK population.FI population.FR population.NL population.NO freq
1      82792351       5781190       5513130      67026224      17181084       5295619   12
2      82792351       5781190       5513130      67026224      17181084       5295619   12
3      82792351       5781190       5513130      67026224      17181084       5295619   12
  epochInPeriod
1    0.08333333
2    0.16666667
3    0.25000000
as.data.frame(IMD6, tidy = TRUE) |> head(3)  # long format
  epoch unit year freq epochInYear epochInPeriod       date observed state alarm upperbound
1     1   DE 1999   12           1    0.08333333 1999-01-01       37 FALSE    NA         NA
2     2   DE 1999   12           2    0.16666667 1999-02-01       48 FALSE    NA         NA
3     3   DE 1999   12           3    0.25000000 1999-03-01       54 FALSE    NA         NA
  population
1   82792351
2   82792351
3   82792351
IMD6DF <- tidy.sts(IMD6)  # the same
## the long data format is convenient for, e.g., custom 'lattice' plots
lattice::xyplot(observed ~ date | unit, data = IMD6DF, type = "h", as.table = TRUE)

## convert to R's basic "ts" (and use its methods)
as.ts(IMD6[1:24,])
         DE DK FI FR NL NO
Jan 1999 37 19  6 43 81  4
Feb 1999 48 17  9 65 68 11
Mar 1999 54 28  5 35 82 10
Apr 1999 36 22  3 30 33  4
May 1999 36 11  7 18 41  6
Jun 1999 30 12  8 19 38  4
Jul 1999 28 13  1 31 45  5
Aug 1999 11  4  5 32 39  6
Sep 1999 22 14  2 32 29  6
Oct 1999 31 12  2 28 34  7
Nov 1999 38 11  5 29 44  8
Dec 1999 31 14  2 49 33  6
Jan 2000 49 20  5 64 68 15
Feb 2000 49 14  5 55 60  5
Mar 2000 56 10 10 38 62  5
Apr 2000 41 17  4 51 40  4
May 2000 37  9  2 45 48  1
Jun 2000 23 13  3 35 36 12
Jul 2000 23 10  3 25 34  5
Aug 2000 24  9  3 26 39  5
Sep 2000 22  8  2 22 22 10
Oct 2000 46 10  3 31 47 10
Nov 2000 46 15  4 48 38  4
Dec 2000 51 16  4 49 46  9
(nlts <- as.ts(IMD6[,"NL"]))
     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999  81  68  82  33  41  38  45  39  29  34  44  33
2000  68  60  62  40  48  36  34  39  22  47  38  46
2001  78  69  58  73  74  47  53  55  40  60  48  83
2002  83  85  85  60  61  45  31  31  22  27  28  49
2003  33  30  50  25  34  22  21  21  23  23  26  36
2004  53  17  26  19  14  21  25  21  12  19  15  25
2005  30  40  28  20  18  16  15  14  13  14  15  23
2006  23  19  18  21   9  13  15   7  12   6  17  11
2007  24  18  19  15  12  14  11  12  20  14  14  13
2008  21  10  11  24  12   0  17  12  13  14  12  16
2009   0  23  23  16   6  10  25  14   7  12  10   4
2010  16   8  12  10   7  23  12  12  14   9  11   9
2011  19  14   5   7  10   8   4   6   6  10   6  11
2012  13  11  11   9  12   7   6   4  12   8   6  11
2013  19  15   7  15   4  11   7   0   7   8   7   8
2014   6   7  11   8   3   6   7   9   9   8   3   6
2015   7   3  16  11   5   5   5   2   4  12   9  11
2016  10  19  18   7   8  11  12  11  11  11  11  23
2017  30  17  25  12  14  10  14  17  10  16  15  18
2018  22  26  27  15  26  10  16  13   7  21  10  13
plot(nlts)

monthplot(nlts)

## if you have package 'xts', this also works:
xts::as.xts(IMD6) |> # then using the plot-method from 'xts'
    plot(multi.panel = TRUE, yaxis.same = FALSE)

Visualization

Try plot(IMD6), but see help(plot.sts) for an overview of the different types of plots available.

The various plot types should provide enough functionality for common visualization needs. More sophisticated plots can always be crafted as needed, based on the provided methods or following as.data.frame(<sts>).

Default type (observed ~ time | unit)

Solution 
## standard plot
plot(IMD6)

## some useful arguments, see ?stsplot_time:
plot(IMD6, same.scale = FALSE)

plot(IMD6, epochsAsDate = TRUE)  # alternative time axis setup

plot(IMD6, units = c("NL","FR")) # select some time series

You could also try the ggplot2 version of this plot using autoplot.sts(). This allows incidences to be plotted rather than counts.

Solution 
autoplot.sts(IMD6)

autoplot.sts(IMD6, scales = "free")

autoplot.sts(IMD6, population = 100000)

autoplot.sts(IMD6, population = 100000, as.one = TRUE)

## or manually based on the tidy.sts() data frame created above:
library("ggplot2")
ggplot(IMD6DF, aes(x = date, y = observed)) + geom_col() + facet_wrap(~ unit)

Overall (observed ~ time)

Solution 
plot(IMD6, type = observed ~ time)

## the same:
IMD1 <- aggregate(IMD6, by = "unit")
plot(IMD1)

autoplot.sts(IMD1)

Map (observed ~ unit)

Produce a map with the country-specific cumulative number of cases (or incidence) over time. See help("stsplot_space") for options.

Solution 
plot(IMD6, type = observed ~ unit)

## only for the year 2018
plot(IMD6, type = observed ~ unit,
     tps = which(year(IMD6) == 2018),
     total.args = list()) # annotate with total number

## cumulative incidence (per 100'000 inhabitants),
## using some of the graphical options
plot(IMD6, type = observed ~ unit, population = 100000,
     gpar.missing = list(lty = 1, col = 8),
     col = "white", lwd = 2, # thick white borders
     sub = "cumulative incidence (per 100'000 inhabitants)")