The ‘Epidemiological Report’ Package

European Centre for Disease Prevention and Control (ECDC)

Description

The EpiReport package allows the user to draft an epidemiological report similar to the ECDC Annual Epidemiological Report (AER) (see https://www.ecdc.europa.eu/en/annual-epidemiological-reports) in Microsoft Word format for a given disease.

Through standalone functions, the package is specifically designed to generate each disease-specific output presented in these reports, using ECDC Atlas export data.

Package details below:

Package Description
Version 1.0.0
Published 2021-01-05
Authors Lore Merdrignac ,
Author of the package and original code

Tommi Karki ,


Esther Kissling ,


Joana Gomes Dias ,
Project manager
Maintainer Lore Merdrignac
License EUPL
Link to the ECDC AER reports https://www.ecdc.europa.eu/en/annual-epidemiological-reports

Background

ECDC’s annual epidemiological report is available as a series of individual epidemiological disease reports. Reports are published on the ECDC website https://www.ecdc.europa.eu/en/annual-epidemiological-reports as they become available.

The year given in the title of the report (i.e. ‘Annual epidemiological report for 2016’) refers to the year the data were collected. Reports are usually available for publication one year after data collection is complete.

All reports are based on data collected through The European Surveillance System (TESSy)1 and exported from the ECDC Atlas. Countries participating in disease surveillance submit their data electronically.

The communicable diseases and related health issues covered by the reports are under European Union and European Economic Area disease surveillance2 3 4 5.

ECDC’s annual surveillance reports provide a wealth of epidemiological data to support decision-making at the national level. They are mainly intended for public health professionals and policymakers involved in disease prevention and control programmes.

1. Datasets to be used in the Epidemiological Report package

1.1. Disease dataset specification

Two types of datasets can be used:

Description of each variable required in the disease dataset (naming and format):

Tab.1 Example of Salmonellosis data 2012-2016
HealthTopicCode MeasurePopulation MeasureCode TimeUnit TimeCode GeoCode XLabel YLabel ZValue YValue N
SALM Confirmed cases CONFIRMED.COUNT M 2013-02 FI NA NA NA 212.0000000 212
SALM Confirmed cases CONFIRMED.AGE.RATE M 2015-06 NL 65+ NA NA 0.8312041 89
SALM Confirmed cases CONFIRMED.AGE.COUNT M 2015-10 EE 5-14 NA NA 2.0000000 7
SALM Confirmed cases CONFIRMED.AGE_GENDER.PROPORTION Y 2014 LV 0-4 Female 26.258993 2.0000000 278
SALM Confirmed cases CONFIRMED.AGE.RATE M 2012-02 LT 25-44 NA NA 1.2746371 60
SALM Confirmed cases CONFIRMED.AGE.PROPORTION M 2014-12 BG 45-64 NA NA NA 0
SALM Confirmed cases CONFIRMED.AGE_GENDER.PROPORTION Y 2012 SK 45-64 Male 6.245948 1.0000000 4627
SALM Confirmed cases CONFIRMED.GENDER.COUNT M 2016-01 DK Male NA NA 38.0000000 81
SALM Confirmed cases CONFIRMED.AGE.COUNT M 2014-10 EE 45-64 NA NA 2.0000000 12
SALM Confirmed cases CONFIRMED.AGE.COUNT M 2016-08 EE 45-64 NA NA 0.0000000 21

1.2. Report parameters dataset specification

The internal dataset EpiReport::AERparams describes the parameters to be used for each output of each disease report.

If the user wishes to set different parameters for one of the 53 covered health topics, or if the user wishes to analyse an additional disease not covered by the default parameter table, it is possible to use an external dataset as long as it is specified as described below and in the help page ?EpiReport::AERparams. All functions of the EpiReport package can then be fed with this specific parameter table.

List of the main parameters included:

Tab.2 Example of the main columns of the parameter dataset
HealthTopic MeasurePopulation TableUse AgeGenderUse TSTrendGraphUse TSSeasonalityGraphUse MapNumbersUse MapRatesUse MapASRUse
VCJD ALL COUNT NO N N N N N
TBE CONFIRMED ASR AG-RATE Y Y N Y N
POLI ALL NO NO N N N N N
CCHF ALL COUNT NO N N N N N
SYPH CONFIRMED RATE AG-RATE N N N Y N

1.3. Member States correspondence table dataset

The internal dataset EpiReport::MSCode provides the correspondence table of the geographical code GeoCode used in the disease dataset, and the geographical label Country to use throughout the report. Additional information on the EU/EEA affiliation is also available in column EUEEA.

Tab.3 Example of geographical codes and associated labels
Country GeoCode EUEEA TheCountry
Norway NO EEA Norway
Slovenia SI EU Slovenia
United Kingdom UK EU the United Kingdom
Hungary HU EU Hungary
Lithuania LT EU Lithuania

2. How to generate the Epidemiological Report in Microsoft Word format

To generate a similar report to the Annual Epidemiological Report, we can use the default dataset included in the EpiReport package presenting Salmonellosis data 2012-2016.

Calling the function getAER(), the Salmonellosis 2016 report will be generated and stored in your working directory (see getwd()) by default.

getAER()

Please specify the full path to the output folder if necessary:

output <- "C:/EpiReport/doc/"
getAER(outputPath = output)

2.1. External disease dataset

To generate the report using an external dataset, please use the syntax below.

In the following example, Pertussis 2016 TESSy data (in csv format, in the /data folder) is used to produce the corresponding report.

Pertussis PNG maps have previously been created and stored in a specific folder /maps.

# --- Importing the dataset
PERT2016 <- read.table("data/PERT2016.csv", 
                       sep = ",", 
                       header = TRUE, 
                       stringsAsFactors = FALSE)

# --- Specifying the folder containing pertussis maps
pathMap <- paste(getwd(), "/maps", sep = "")


# --- (optional) Setting the local language in English for month label
Sys.setlocale("LC_TIME", "C")
#> [1] "C"

# --- Producing the report
EpiReport::getAER(disease = "PERT", 
       year = 2016, 
       x = PERT2016, 
       pathPNG = pathMap)

Please note that the font Tahoma is used in the plot axis and legend. It is advised to import this font using the extrafont package and the command font_import and loadfonts.

However, if the users prefer the use of the default Arial in plots, it is optional. In that case, warnings will appear in the console for each plot.

2.2. Word template

By default, an empty ECDC template (Microsoft Word) is used to produce the report. In order to modify this template, please first download the default template using the function getTemplate().

You can store this Microsoft Word template in a specific folder /template.

getTemplate(output_path = "C:/EpiReport/template")

Then, apply the modifications required, save it and use it as a new Microsoft Word template when producing the epidemiological report as described below.

getAER(template = "C:/EpiReport/template/New_AER_Template.docx",
       outputPath = "C:/EpiReport/doc/")

Please make sure that the Microsoft Word bookmarks are preserved throughout the modifications to the template. The bookmarks specify the location where to include each output.

2.3. Word bookmarks

Each epidemiological output will be included in the Word template in the corresponding report chapter. The EpiReport package relies on Microsoft Word bookmarks to specify the exact location where to include each output.

The list of bookmarks recognised by the EpiReport package are:

3. How to generate each epidemiological outputs independently

The EpiReport package allows the user to generate each epidemiological output independently of the Microsoft Word report.

The ECDC annual epidemiological Report includes five types of outputs:

3.1. Table: distribution of cases by Member State

The function getTableByMS() generates a flextable object (see package flextable) presenting the number of cases by Member State over the last five years.

By default, the function will use the internal Salmonellosis 2012-2016 data and present the number of confirmed cases and the corresponding rate for each year, with a focus on 2016 and age-standardised rates.

EpiReport::getTableByMS()

Country

2015

2016

2017

2018

2019

Number

Rate

Number

Rate

Number

Rate

Number

Rate

Number

Rate

ASR

Austria

103

1.2

116

1.3

85

1.0

85

1.0

142

1.6

1.7

Belgium

108

1.0

114

1.0

77

0.7

101

0.9

202

1.8

1.9

Bulgaria

.

.

.

.

.

.

.

.

.

.

.

Croatia

-

-

2

0.0

0

0.0

2

0.0

4

0.1

0.1

Cyprus

.

.

.

.

.

.

.

.

.

.

.

Czechia

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0.0

Denmark

.

.

.

.

.

.

.

.

.

.

.

Estonia

12

0.9

9

0.7

8

0.6

6

0.5

6

0.5

0.5

Finland

54

1.0

66

1.2

25

0.5

56

1.0

81

1.5

1.6

France

285

0.4

373

0.6

266

0.4

331

0.5

904

1.3

1.4

Germany

722

0.9

958

1.2

635

0.8

614

0.7

1175

1.4

1.5

Greece

2

0.0

2

0.0

1

0.0

2

0.0

10

0.1

0.1

Hungary

12

0.1

24

0.2

17

0.2

14

0.1

44

0.5

0.5

Iceland

0

0.0

0

0.0

1

0.3

1

0.3

4

1.1

1.1

Ireland

8

0.2

18

0.4

10

0.2

17

0.4

18

0.4

0.4

Italy

103

0.2

106

0.2

95

0.2

108

0.2

231

0.4

0.4

Latvia

4

0.2

9

0.5

13

0.7

12

0.6

11

0.6

0.6

Liechtenstein

.

.

.

.

.

.

.

.

.

.

.

Lithuania

9

0.3

4

0.1

4

0.1

8

0.3

9

0.3

0.4

Luxembourg

0

0.0

1

0.2

0

0.0

1

0.2

1

0.2

0.2

Malta

1

0.2

1

0.2

3

0.7

1

0.2

2

0.4

0.4

Netherlands

18

-

6

-

0

-

0

-

0

-

-

Norway

98

1.9

64

1.2

35

0.7

49

0.9

102

1.9

2.0

Poland

12

0.0

41

0.1

29

0.1

30

0.1

55

0.1

0.1

Portugal

14

0.1

13

0.1

11

0.1

14

0.1

30

0.3

0.3

Romania

7

0.0

8

0.0

7

0.0

4

0.0

15

0.1

0.1

Slovakia

2

0.0

4

0.1

2

0.0

7

0.1

6

0.1

0.1

Slovenia

3

0.1

6

0.3

5

0.2

8

0.4

21

1.0

1.1

Spain

168

0.4

261

0.6

128

0.3

205

0.4

228

0.5

0.5

Sweden

159

1.6

225

2.3

106

1.1

106

1.0

235

2.3

2.4

United Kingdom

423

0.7

468

0.7

465

0.7

432

0.7

827

1.2

1.3

EU-EEA

2327

0.5

2899

0.6

2028

0.4

2214

0.5

4363

0.9

0.9

Table. Distribution of confirmed salmonellosis cases, EU/EEA, 2012-2016

This table can be drafted using external data, and specifying the disease code and the year to use as reference in the report.

In the example below, we use Zika virus data. According to the report parameters, the table for this disease should present the number of reported cases over the last five years and by Member State.

ZIKV2016 <- read.table("data/ZIKV2016.csv", 
                       sep = ",", 
                       header = TRUE, 
                       stringsAsFactors = FALSE)
EpiReport::getTableByMS(x = ZIKV2016, 
             disease = "ZIKV", 
             year = 2016)

Country

2012

2013

2014

2015

2016

Number

Number

Number

Austria

-

-

-

1

41

Belgium

-

-

-

1

120

Bulgaria

.

.

.

.

.

Croatia

.

.

.

.

.

Cyprus

.

.

.

.

.

Czechia

-

-

-

-

13

Denmark

-

-

-

-

8

Estonia

-

-

-

-

0

Finland

-

-

-

1

6

France

-

-

-

-

1141

Germany

.

.

.

.

.

Greece

-

-

-

-

4

Hungary

-

-

-

-

2

Iceland

.

.

.

.

.

Ireland

-

-

-

1

15

Italy

-

-

-

-

101

Latvia

0

0

0

0

0

Liechtenstein

.

.

.

.

.

Lithuania

.

.

.

.

.

Luxembourg

-

-

-

-

2

Malta

-

-

-

-

2

Netherlands

-

-

-

11

98

Norway

-

-

-

-

8

Poland

.

.

.

.

.

Portugal

-

-

-

-

18

Romania

-

-

-

-

3

Slovakia

-

-

-

-

3

Slovenia

-

-

-

-

7

Spain

-

-

-

10

301

Sweden

-

-

-

1

34

United Kingdom

-

-

-

3

194

EU-EEA

0

0

0

29

2121

Table. Distribution of Zika virus infection cases, EU/EEA, 2012-2016

3.2. Seasonality plot: distribution of cases by month

The function getSeason() generates a ggplot (see package ggplot2) presenting the distribution of cases at EU/EEA level, by month, over the past five years.

The plot includes:

By default, the function will use the internal Salmonellosis 2012-2016 data.

# --- Salmonellosis 2016 plot
EpiReport::getSeason()

Figure. Distribution of confirmed salmonellosis cases by month, EU/EEA, 2016 and 2012-2015

The plot can also be drafted using external data, and specifying the disease dataset, the disease code and the year to use as reference in the report.

In the example below, we use Pertussis 2012-2016 data.

# --- Pertussis 2016 plot
EpiReport::getSeason(x = PERT2016,
                     disease = "PERT",
                     year = 2016)

Figure. Distribution of pertussis cases by month, EU/EEA, 2016 and 2012-2015

3.3. Trend plot: trend and number of cases by month

The function getTrend() generates a ggplot (see package ggplot2) presenting the trend and the number of cases at EU/EEA level, by month, over the past five years.

The plot includes:

By default, the function will use the internal Salmonellosis 2012-2016 data.

# --- Salmonellosis 2016 plot
EpiReport::getTrend()

Figure. Trend and number of confirmed salmonellosis cases, EU/EEA by month, 2012-2016

The plot can also be drafted using external data, and specifying the disease dataset, the disease code and the year to use as reference in the report.

In the example below, we use again Pertussis 2012-2016 data.

# --- Pertussis 2016 plot
EpiReport::getTrend(x = PERT2016,
                    disease = "PERT",
                    year = 2016)

Figure. Trend and number of pertussis cases, EU/EEA by month, 2012-2016

3.4. Maps: distribution of cases by Member State

The function getMap() provides with a preview of the PNG map associated with the disease.

By default, the function will use the internal Salmonellosis 2016 PNG maps. According to the report parameters, the corresponding map should present the notification rate of confirmed salmonellosis cases.

# --- Salmonellosis 2016 map
EpiReport::getMap()