The ‘Epidemiological Report’ Package

1.1. Disease dataset specification

Two types of datasets can be used:

• The default dataset included in the EpiReport package which includes Salmonellosis data for 2012-2016: EpiReport::SALM2016;
• Any dataset specified as described below.

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

• HealthTopicCode: Character string, disease code (see also the reports parameter table Tab.3);
• MeasurePopulation: Character string, population characteristics (e.g. All cases, Confirmed cases, Serotype AGONA, Serotype BAREILLY etc.).
• MeasureCode: Character string, code of the indicators available (e.g. ALL.COUNT, ALL.RATE, CONFIRMED.AGESTANDARDISED.RATE etc.);
• TimeUnit: Character string, unit of the time variable TimeCode (e.g. M for monthly data, Y for yearly data).
• TimeCode: Character string, time variable including dates in any formats available i.e. yearly data (e.g. 2001) or monthly data (e.g. 2001-01);
• GeoCode: Character string, geographical level in coded format (e.g. AT for Austria, BE for Belgium, BG for Bulgaria, see also the EpiReport::MSCode table, correspondence table for Member State labels and codes);
• XLabel: The label associated with the x-axis in the epidemiological report (see getAgeGender() and plotAgeGender() bar graph for the age variable);
• YLabel: The label associated with the y-axis in the epidemiological report (see getAgeGender() and plotAgeGender() bar graph for the grouping variable gender);
• ZValue: The value associated with the stratification of XLabel and YLabel in the age and gender bar graph (see getAgeGender() and plotAgeGender());
• YValue: The value associated with the y-axis in the epidemiological report (see plotAge bar graph for the variable age, or getTableByMS() for the number of cases, rate or age-standardised rate in the table by Member States by year);
• N: Integer, number of cases (see getTrend() and getSeason() line graph).

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:

• HealthTopic: Character string, disease code that should match with the health topic code from the disease-specific dataset (see Tab.1)
• MeasurePopulation: Character string, population to present in the report: either ALL cases or CONFIRMED cases only.
• TableUse: Character string, specifying whether to include the table in the epidemiological report and which table to include:
  • NO: No table included
  • COUNT: Table presenting the number of cases by Member State by year
  • RATE: Table presenting the rates of cases by Member State by year
  • ASR: Table presenting the age-standardised rates of cases by Member State by year
• AgeGenderUse: Character string, specifying whether to include the age and gender bar graph in the epidemiological report and which type of graph to include:
  • NO: No graph included
  • AG-COUNT: Bar graph presenting the number of cases by age and gender
  • AG-RATE: Bar graph presenting the rates of cases by age and gender
  • AG-PROP: Bar graph presenting the proportion of cases by age and gender
  • A-RATE: Bar graph presenting the rates of cases by age
• TSTrendGraphUse: Yes/No, specifying whether to include a line graph describing the trend of the disease over the time.
• TSSeasonalityGraphUse: Yes/No, specifying whether to include a line graph describing the seasonality of the disease.
• MapNumbersUse: Yes/No, specifying whether to include the map presenting the number of cases by Member State.
• MapRatesUse: Yes/No, specifying whether to include the map presenting the rates of cases by Member State.
• MapASRUse: Yes/No, specifying whether to include the map presenting the age-standardised rates of cases by Member State.

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.

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)
#> 5
#> _x0000_t202
#> Text Box 2
#> 1
#> 0
#> 

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.

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()

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)

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:

• The number of cases by month in the reference year (green solid line)
• The mean number of cases by month in the four previous years (grey dashed line)
• The minimum number of cases by month in the four previous years (grey area)
• The maximum number of cases by month in the four previous years (grey area)

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

# --- Salmonellosis 2016 plot
EpiReport::getSeason()
#> Warning: Use of `data[[min4years]]` is discouraged. Use `.data[[min4years]]`
#> instead.
#> Warning: Use of `data[[max4years]]` is discouraged. Use `.data[[max4years]]`
#> instead.
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.
#> Warning: Use of `data[[mean4years]]` is discouraged. Use `.data[[mean4years]]`
#> instead.
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.
#> Warning: Use of `data[[yvar]]` is discouraged. Use `.data[[yvar]]` instead.
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.

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)
#> Warning: Use of `data[[min4years]]` is discouraged. Use `.data[[min4years]]`
#> instead.
#> Warning: Use of `data[[max4years]]` is discouraged. Use `.data[[max4years]]`
#> instead.
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.
#> Warning: Use of `data[[mean4years]]` is discouraged. Use `.data[[mean4years]]`
#> instead.
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.
#> Warning: Use of `data[[yvar]]` is discouraged. Use `.data[[yvar]]` instead.
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.

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:

• The number of cases by month over the 5-year period (grey solid line)
• The 12-month moving average of the number of cases by month (green solid line)

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

# --- Salmonellosis 2016 plot
EpiReport::getTrend()
#> Warning: Use of `data[[yvar]]` is discouraged. Use `.data[[yvar]]` instead.
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.
#> Warning: Use of `data[[movAverage]]` is discouraged. Use `.data[[movAverage]]`
#> instead.
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.

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)
#> Warning: Use of `data[[yvar]]` is discouraged. Use `.data[[yvar]]` instead.
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.
#> Warning: Use of `data[[movAverage]]` is discouraged. Use `.data[[movAverage]]`
#> instead.
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.

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()

Figure. Distribution of confirmed salmonellosis cases per 100 000 population by country, EU/EEA, 2016

The map can also be included using external PNG files, and specifying the disease code and the year to use as reference in the report. The corresponding syntax is described below (pertussis map not available).

# --- Pertussis 2016 map
EpiReport::getMap(disease = "PERT", 
                  year = 2016, 
                  pathPNG = "C:/EpiReport/maps/")

3.5. Age and gender bar graph

The function getAgeGender() generates a ggplot (see package ggplot2) presenting in a bar graph the distribution of cases at EU/EEA level by age and gender.

The bar graph uses either:

• The number of cases,
• The rate per 100 000 cases,
• The proportion of cases.

By default, the function will use the internal Salmonellosis 2012-2016 data with the rate of confirmed cases per 100 000 population.

# --- Salmonellosis 2016 bar graph
EpiReport::getAgeGender()
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.
#> Warning: Use of `data[[yvar]]` is discouraged. Use `.data[[yvar]]` instead.
#> Warning: Use of `data[[group]]` is discouraged. Use `.data[[group]]` instead.

Figure. Distribution of confirmed salmonellosis cases per 100 000 population, by age and gender, EU/EEA, 2016

The bar graph 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 Zika 2012-2016 data.

# --- Zika 2016 bar graph
EpiReport::getAgeGender(x = ZIKV2016, 
                        disease = "ZIKV", 
                        year = 2016)
#> Warning: Use of `data[[xvar]]` is discouraged. Use `.data[[xvar]]` instead.
#> Warning: Use of `data[[yvar]]` is discouraged. Use `.data[[yvar]]` instead.
#> Warning: Use of `data[[group]]` is discouraged. Use `.data[[group]]` instead.

Figure. Distribution of Zika virus infection proportion (%), by age and gender, EU/EEA, 2016