# Guide: Measles outbreak report

This guide was written by Applied Epi. Feedback and suggestions are welcome at the GitHub issues page

# 1 Introduction

## 1.1 Purpose of this guide

This guide accompanies the measles outbreak report .Rmd file, which can be used to create an automated outbreak report for measles.

## 1.2 Who this guide is for

This guide and the sitrep code is intended for individuals who already have some familiarity with R but want ready-made code to make the report production process faster. You need to be able to edit and troubleshoot code.

## 1.3 Outbreak report contents

The report will contain basic information on person, place, and time, specifically:

- An overall epi summary with bullet points and epicurve
- Age and sex distribution
- Attack rates and case fatality ratios by age group and geographical areas
- Vaccination history and nutritional status
- Maps and more detailed geographical breakdowns

# 2 Instructions

## 2.1 Structure of the outbreak report Rmd

The outbreak report Rmd is split up into sections with chunks which relate to:

1. Report set-up including package installation and setting definitions
2. Importing data
3. Cleaning data including standardising categorical values, removing illogical values, removing unnecessary rows and columns, and creating columns needed for analysis
4. Analysis

Only the outputs from the fourth section on analysis will be visible in the report when rendering it.

Note there are comments throughout the Rmd file which refer to the relevant sections in this guide. The comments look like this:

    <!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    Comments are shown in the code between these special lines  They will not appear in the report output
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

## 2.2 How to produce the report

With the help of this guide (specifically section 3), you should produce the report via the following steps:

1. Go through the outbreak template .Rmd in detail and make edits as needed. Make sure you run the code within chunks and inspect the data as you go, to make sure you correctly and appropriately edit the code. There are several sections where code edits are required (highlighted in this guide in yellow) or expected (highlighted in this guide in green):
   1. ⚠️Required: The YAML, to specify the title, location, MSF office, and date of report.
   2. ⚠️Required: The `definitions` chunk, to correctly define the reporting date and other key objects. Make sure the date here matches the one in the YAML.
   3. ⚠️Required: The data import section, to import the correct data
   4. ⚠️Required: The recommendations chunk, to add text based on the results.
   5. ⚠️Expected: The various data cleaning chunks, to ensure that data is appropriate for the report.
   6. ⚠️Expected: The analysis chunks, to ensure the analysis or presentation is appropriate for the data. In particular:
      1. Make sure you use the correct date for epicurves and other temporal analyses. The default uses a column that prioritises onset date if available and substitutes with notification date if onset is not available. It may be more appropriate to use only onset date or only notification date, depending on your data e.g. completeness and reliability of both.
      2. Ensure that missing values are correctly used. The default is that “Unknown” or missing (NA) values are presented as “[Missing]” in tables and are excluded from denominators.
      3. Ensure the correct geography columns are being used. The data has three levels (adm1, adm2, and adm3). By default, most geographical breakdowns in this report (tables and maps) use adm2 level, there is a detailed table for cases by adm3 level at the end, and adm1 is not used.
      4. Adjust the map legend as needed so that the categories are appropriate (explained further in relevant section)
      5. Adjust other presentation features, such as column widths in tables, the space between date labels in the epicurve x axis, or the `fig.height` option in chunk labels to change the height of figures.
2. When you are happy with the Rmd code, click “knit” at the top, which will produce the outbreak report.
3. Take extra steps in MS Word to activate the table of contents: When opening the produced word document, click “Yes” in the popup asking “Do you want to update the fields in this document?”. Then click on “Update Table…” at the top left of the table of contents and update the page numbers, so that they accurately represent the location of each section. See below:

## 2.3 Requirements for report generation

You will need:

- A measles linelist, with the following requirements:
  - One row per case
  - Information on: sex, age, geographic location, date of notification, symptom onset date, vaccination status, malnutrition level, and illness outcome
  - Column names and categorical values that align with the MSF intersectional linelist
- Population data for age groups and geographic areas of interest, if you want to create the case rates per area.

If you need to change your data into this format, do so with the outbreak report recode Rmd file first

# 3 Detailed guide

## 3.1 YAML

At the very top of the Rmd file, make sure you specify the following by changing the “XXX” content within the quotations marks: - The title (default is “Measles outbreak report”) - The location/area that the outbreak report concerns - The MSF office - The date of the report

Make sure you do not remove the quotation marks or make edits outside of the quotation marks.

## 3.2 Set up and load data

### 3.2.1 `setup` chunk

⚠️ You will need to edit this chunk.

This chunk first sets up key preferences for this R Markdown file, in `opts_chunk$set()`. By default it is set to not show code, to show errors, or warning messages in the output.

This chunk also installs/loads packages. Several packages are required for different aspects of analysis with R. You will need to install these before starting.

### 3.2.2 `definitions` chunk

⚠️ You will need to edit this chunk.

This chunk is for inputting information so that the report is suited to this dataset.

1. Set what the three geographical objects `adm1_residence_name`, `adm2_residence_name`, `adm3_residence_name`, mean. For example, they may represent the province, district, and village of each case respectively, but this will differ depending on the data.

2. Set the date of the report, assuming the report is updated on a weekly basis.

   1. Provide the actual date of report in YYYY-MM-DD format.
   2. Edit the `yearweek` function if necessary, so that it correctly defines the week that this sitrep is reporting on, e.g. “2024 W52”. The default argument for week start inside `yearweek` is 1, which means it assumes reporting weeks start on Mondays. You can change it to 2 for Tuesday, 3 for Wednesday etc.

### 3.2.3 `set_plot_defaults` chunk

This code set the defaults for epicurve plots, such as the base text size, the text angle, legend title, gridline color (grey), and x and y labels. It also standardises the labels of epicurves.

## 3.3 Import data

### 3.3.1 `read_population_data` chunk

⚠️ You will need to edit this chunk.

This report template uses population data to calculate attack rates.

This chunk creates two objects that are used later in the code:

- population_data_age
- population_data_adm2 *

For each object, there are three options to create these objects:

1. If you have files that contain denominator information, read them in (note the code is reading in excel files as default)
2. If you have counts per subgroup, use the `gen_population()` function from the `{sitrep}` package to combine into a table.
3. If you have the total populations and population distributions, use the `gen_population()` function to generate sub-group specific counts.

Comment out or delete the options you are not using, and edit the one you will use.

* Note this is for calculating rates for the adm2 level. If you need rates for adm1 or adm3 geographic levels, load the relevant population data in the appropriate way, and change the object name for clarity (e.g. to `population_data_adm3`), or create an additional appropriately named denominator data objects.

### 3.3.2 `read_data` chunk

⚠️ You will need to edit this chunk.

This chunk loads linelist data into RStudio. There are seven options you can pick between to load data. Delete or comment out the code that you do not need, and edit the code you do need by specifying the correct file name and location:

1. Load an example clean linelist in intersectional data format using the `gen_data()` function from the `{sitrep}` package. Do this if you want to see what the clean data should look like, or if you want to test the outbreak template
2. Load data from an excel file within a specific sheet
3. Load data from an excel file with macros (this requires read_excel)
4. Load data from an excel file with a particular range of cells
5. Load data from an excel file within a particular sheet but also with a password. Note this needs the installation of some additional packages
6. Load data from a csv file
7. Load data from a stata file

### 3.3.3 `browse_data` chunk

You’ll want to look at your data.

This chunk shows you a few ways you can explore, including printing out a table which shows all values in your columns (excluding the case ID or health facility IDs which would be too many to tabulate) so that you can identify non-standardised or incorrect values.

It is recommended you check other detail more comprehensively as well.

Note that these tables will not be printed when you run the report.

## 3.4 Clean linelist data

All your cleaning and variable creation should happen in these chunks:

Step | Description
--- | ---
`standardise_dates` | Sets up and cleans dates, and creates new columns on reporting weeks. This includes creating a new data_combined column which prioritises onset date and uses notification date if onset date is not available
`create_age_group` | Creates the age group column from age (and some intermediary columns). For measles, two age group columns are created for a choice of table outputs in the analysis section
`standardise_values` | Cleans the values of categorical variables (e.g., typo correction) and removes illogical values.
`create_vars` | Creates additional columns based on other columns, and converts categorical columns into ordered factors so that all values are presented in the correct order in table outputs
`duplicates` | Removes duplicate rows.
`remove_unused_data` | Removes rows (and columns) that are not required for analysis.
`save_cleaned_data` | Saves the clean data as a back-up.

⚠️ You will likely need to edit some of these chunks.

### 3.4.1 `standardise_dates` chunk

As the data should already be in the right format, you should not need to change this section. If you have imported an RDS file, this code might not be needed, but it will be useful for excel files and csv files etc.

The code does the following:

1. Changes all columns starting with the word ‘date’ (i.e. the `date_notification`, `date_symptom_start`, and `date_hospitalisation_end` columns) to be recognised as dates by R. The function `ymd` is used to recognise that the text has dates written in the order of year, month, and day (e.g. 2025-02-01 or 2025 Feb 01 or 2025 February 1). ⚠️ Change this if the dates are written in a different way in your data, e.g. to `dmy()` if written in day, month, year (e.g. 01-02-2025 or 1 Feb 2025)

2. Fixes logical inconsistent dates: removed symptom onset dates if they are after notification dates. ⚠️ Inspect and edit data if you can, or change the rule to fit the circumstances of your data

3. Creates a new date column, `date_combined`, to maximise date completeness, by using the `coalesce()` function that prioritises symptom onset date, and substitutes with notification date if onset date is not available. It also creates a `date_source` column to label if the value in the `date_combined` column is the onset date or the notification date.

4. Creates six new week columns:

   - `epiweek_symptom_start_num`, `epiweek_notification_num`, and `epiweek_combined_num`: These are week representations of the onset date, notification date, and `date_combined` columns, and are represented with the year and the week number, e.g. “2025 W10”. It uses the `yearweek` function in which you state which day the week starts (default 1 for Monday). These are not the default columns used in epicurves but these can be switched to.

- `epiweek_symptom_start`, `epiweek_notification`, and `epiweek_combined`: This will create corresponding columns with the date representing the start of the week, e.g. “2025-03-03”. Note the selected date will depend on the start day provided in the `yearweek` function.

### 3.4.2 `create_age_group` chunk

This chunk creates categorical age groups, representing age groups typically analysed for the disease. For measles, two options are created.

### 3.4.3 `standardise_values` chunk

Edit the code as needed to make sure values are standardised and correct. The checks you did in the `browse_data` chunk will inform this section.

Two examples are provided to start with:

- Correct geographical values: The template shows the correction of the adm2_residence column, first by standardizing the capitalization, and then specifying which typos should be corrected to which values.
- Changing all “Unknown” and “” values to NA (recognised as missing by R), across all character columns. This is so that these values are not included in the denominator for analyses looking at percentage distributions. ⚠️Consider changing or removing this if you want to handle unknown values differently

This template does not include code for all possible errors as this will depend on your data, so you may need to write your own code/consult someone who can write code in R to make sure your linelist data is fully ready for analysis.

### 3.4.4 `create_vars` chunk

This chunk creates other columns used in analysis, and converts categorical columns into a factor class so that all categories are displayed in the correct order in tables later in the analysis. You can edit this section for more columns.

New columns are:

- `died`: binary (TRUE/FALSE) column labeling if a case died or not.

These columns are changed into factors. Note that values not specified as valid categories in the code will be converted to NA:

- `vacci_measles_yn`
- `vacci_measles_doses`
- `muac_adm`
- `date_source`

### 3.4.5 `duplicates` chunk

This chunk removes duplicate cases, presenting two options. You can edit to use whichever unique identifiers you think relevant.

- Option 1 simply keeps the first occurrence of a duplicated case based on case_number, sex and age_group. In the default template, the deduplication removes repeat rows for an individual with the same case_id, sex_id, and age_group.
- Option 2 gives you the ability to create a TRUE/FALSE variable to flag rows that are duplicated - giving you more flexibility around browsing which ones to drop.

### 3.4.6 `remove_unused_data` chunk

This step filters out data that are not appropriate to include in the analysis, for instance:

- Data with onset after the reporting week: This removes cases that are not feasible for this report

- Other ineligible/anomalous data: E.g. you might want to remove rows with missing essential data. Use this section to make other edits as needed (please do not change column names or formats).

### 3.4.7 `save_cleaned_data` chunk

You can save your cleaned dataset as an RDS to keep the correct class/format for all columns. The template code automatically names your file “linelist_cleaned_DATE”, where DATE is the current date. Uncomment this code to use it.

## 3.5 Analysis

### 3.5.1 Recommendations text

⚠️ You will need to edit this section either in the code or final output.

This is a placeholder section for you to add comments in consultation with the appropriate team/expertise.

### 3.5.2 Summary text

At the start of the epi description, there are some short bullet points describing the number of cases and key epi points.

### 3.5.3 `epicurve` chunk

This chunk starts by creating the objects `all_weeks` and `all_weeks_date` with all weeks, spanning from the earliest `epiweek_combined` value to the `reporting_date`. This is used across epicurves in this report to define the range in the x axis.

Then the epicurve is created using the coalesced column `epiweek_combined`. The source of the date is indicated by the fill colour of the bars, based on the `date_source` column. Note that the title of this epicurve specifies onset date, so this analysis and code assumes that the `epiweek_combined` column is indeed mostly onset date and only sometimes approximated by notification date. ⚠️ Change the week column used if not appropriate, e.g. if there is high missingness in onset date.

Change the `fig.height` chunk option for smaller or larger figures, and change the `breaks` argument in the `scale_x_date()` function to specify the time difference between the x axis date labels. For example, you can specify `breaks = "1 week"` or `breaks = "2 months"`.

### 3.5.4 Age and sex distribution

This section produces bullet points at the top to summarise key points about age and sex distribution, plus options for tables and age-sex pyramids to pick from.

⚠️ You will need to pick the most suitable age-breakdown for the report context.

- `total_props_agegroup_sex` and `total_props_agegroup_sex_detailed` chunks: These produce age-sex breakdown tables with varying age-group granularity. Delete as needed.
- `age_pyramid` and `age_pyramid_detailed` chunks: These produce the age-sex pyramids, again as two options with varying age-group granularity. Delete as needed. Note the `age_pyramid()` function produces a ggplot object so can be further edited with `{ggplot2}` code if needed (e.g themes, labels, and scales).

### 3.5.5 Combined count, attack rate, and case fatality ratio tables

This section calculates total case counts, cases in the last 14 days (calculated for the 14 days prior to reporting date), deaths, and the CFR. Both attack rate tables link to the populations table imported or produced in the `read_population_data` chunk.

- The `attack_rate_by_agegroup` chunk creates a table by broad age group using the `age_group` column
- The `attack_rate_by_adm2` chunk creates a table by unique value for `adm2_residence`.

### 3.5.6 Other characteristics and outcomes chunks

The `vaccination_nutritional` chunk produces a table with `tbl_summary()` showing vaccination status and nutritional status, by broad age group.

The `vaccination_doses` chunk produces a table restricted to those who were previously vaccinated, showing the number of doses they had received by broad age group.

The `outcomes` chunk, finally, shows the outcomes of cases by broad age group.

### 3.5.7 Geographic distribution - tables

Two chunks produce the following:

- `describe_by_adm2` chunk: A table with geographical breakdowns, for the adm2 level, by age group.
- `epicurve_by_adm2` chunk: This epicurve uses the same code as the first main epicurve, with the addition of a `facet_wrap()` function to split the code into several mini plots per `adm2` value. You may want to edit the figure height and width within the chunk so that it fits on the page. As with the main epicurve, you may also want to make sure you use the right week column, as this analysis and code assumes that the `epiweek_combined` column is mostly onset date and only sometimes approximated by notification date. ⚠️ Change the week column used if not appropriate, e.g. if there is high missingness in onset date.

Note that the meaning of adm2, e.g. District vs Region, should be set in the `definitions` chunk so that the titles and labels within these tables and figures are correct.

### 3.5.8 Geographic distribution - maps

The report produces three maps to show:

- Attack rates by adm2
- Total cases by adm2
- Cases in last 14 days by adm2

The map production is split up into several chunks:

- `read_shapefiles`: To create maps, you need to have a shapefile of the area (note that a shapefile typically consists of several files, of which one ends in .shp). This chunk gives you the option of generating a fake shape file with the `gen_polygon` function. Otherwise, you can read in the shapefile. Often, the MSF GIS unit can provide shapefiles and advice on how to use them.Your shapefile can be a polygon or points. Polygons do not need to be contiguous. The names of the polygons or points MUST match the names in your linelist. Finally, your coordinate reference system needs to be WGS84.
- `chloropleth_map_prep`: This chunk builds on the detailed attack rate table produced in the `attack_rate_by_adm2` chunk, by converting counts and attack rates into categories suitable for mapping. The `find_breaks()` function is used to dynamically define the boundaries of the groupings. Edit the `breaks = X` and `snap = X` argument to change how many and how wide the subgroups are. For example, if there are 5000 total cases, you may want four categories (X=4) that snap to the closest 500 (snap=500, for example for categories 0, 1-1000, 1001-2000, 2001-3000, and 3001+). This chunk also links to the `map` object created in the `read_shapefiles` chunk to bring in the geometries for mapping. ⚠️ You will likely need to edit this chunk to change the categories that appear on the map/legend.
- `chloropleth_map` chunks: Three chunks create one map each.

### 3.5.9 Geographic detail

The `describe_by_adm2_adm3` chunk creates a table showing the distribution of cases by adm2 and adm3. It has the potential to be a long table so it is at the end of the report.

The meanings of adm2 and adm3 are set at the top in the `definitions` chunk, which as default are sets to mean district and village respectively. Go back and change this chunk if the table titles are incorrect.