Aggregate and visualize

Last updated on 2024-11-14 | Edit this page

Estimated time: 30 minutes

Overview

Questions

  • How to aggregate case data?
  • How to visualize aggregated data?
  • What is distribution of cases in time, place, gender, age?

Objectives

  • Simulate synthetic outbreak data
  • Convert linelist data to incidence
  • Create epidemic curves from incidence data

Introduction

In an analytic pipeline, exploratory data analysis (EDA) is an important step before formal modelling. EDA helps determine relationships between variables and summarize their main characteristics often by means of data visualization.

This episode focuses on EDA of outbreak data using a few essential R packages. A key aspect of EDA in epidemic analysis is identifying the relationship between time and the observed epidemic outcome, such as confirmed cases, hospitalizations, deaths, and recoveries across different locations and demographic factors, including gender, age, and more.

Let’s start by loading the package incidence2 to aggregate linelist data by groups and visualize epicurves. We’ll use {simulist} to simulate outbreak data, and {tracetheme} for complementary figure formatting. We’ll use the pipe %>% to connect some of their functions, including others from the packages dplyr and ggplot2, so let’s also call to the tidyverse package:

R 

# Load packages
library(incidence2) # For aggregating and visualising
library(simulist) # For simulating linelist data
library(tracetheme) # For formatting figures
library(tidyverse) # For {dplyr} and {ggplot2} functions and the pipe %>%

The double-colon

The double-colon :: in R let you call a specific function from a package without loading the entire package into the current environment.

For example, dplyr::filter(data, condition) uses filter() from the dplyr package. This help us remember package functions and avoid namespace conflicts.

Synthetic outbreak data

To illustrate the process of conducting EDA on outbreak data, we will generate a line list for a hypothetical disease outbreak utilizing the {simulist} package. {simulist} generates simulation data for outbreak according to a given configuration. Its minimal configuration can generate a linelist as shown in the below code chunk

R 

# Simulate linelist data for an outbreak with size between 1000 and 1500
set.seed(1) # Set seed for reproducibility
sim_data <- simulist::sim_linelist(outbreak_size = c(1000, 1500)) %>%
  dplyr::as_tibble() # for a simple data frame output

WARNING 

Warning: Number of cases exceeds maximum outbreak size.
Returning data early with 1546 cases and 3059 total contacts (including cases).

R 

# Display the simulated dataset
sim_data

OUTPUT 

# A tibble: 1,546 × 12
      id case_name       case_type sex     age date_onset date_admission outcome
   <int> <chr>           <chr>     <chr> <int> <date>     <date>         <chr>
 1     1 Kaylin Alberts  probable  f        70 2023-01-01 2023-01-06     recove…
 2     3 Guirnalda Azuc… probable  f        25 2023-01-11 2023-01-18     died
 3     6 Kevin Lee       suspected m        80 2023-01-18 NA             recove…
 4     8 Ashraf al-Raha… probable  m         8 2023-01-23 2023-02-01     recove…
 5    11 Jacob Miller    probable  m        69 2023-01-30 NA             recove…
 6    14 Rocky Bustillos suspected m        40 2023-01-24 2023-01-29     recove…
 7    15 Jim Soriano     confirmed m        37 2023-01-31 NA             recove…
 8    16 Abdul Wadood e… suspected m        67 2023-01-30 NA             recove…
 9    20 Kristy Neish    probable  f        57 2023-01-27 NA             recove…
10    21 Azeema al-Shab… confirmed f        70 2023-02-09 2023-02-13     died
# ℹ 1,536 more rows
# ℹ 4 more variables: date_outcome <date>, date_first_contact <date>,
#   date_last_contact <date>, ct_value <dbl>

This linelist dataset offers individual-level information about the outbreak.

This is the default configuration of {simulist}, if you want to know more about its functionalities check the documentation website.

You can also find data sets from real emergencies from the past at the {outbreaks} R package.

Aggregating

Downstream analysis involves working with aggregated data rather than individual cases. This requires grouping linelist data in the form of incidence data. The incidence2 package offers an essential function, called incidence2::incidence(), for grouping case data, usually centered around dated events and/or other factors. The code chunk provided below demonstrates the creation of an <incidence2> class object from the simulated Ebola linelist data based on the date of onset.

R 

# Create an incidence object by aggregating case data based on the date of onset
dialy_incidence <- incidence2::incidence(
  sim_data,
  date_index = "date_onset",
  interval = "day" # Aggregate by daily intervals
)

# View the incidence data
dialy_incidence

OUTPUT 

# incidence:  232 x 3
# count vars: date_onset
   date_index count_variable count
   <date>     <chr>          <int>
 1 2023-01-01 date_onset         1
 2 2023-01-11 date_onset         1
 3 2023-01-18 date_onset         1
 4 2023-01-23 date_onset         1
 5 2023-01-24 date_onset         1
 6 2023-01-27 date_onset         2
 7 2023-01-29 date_onset         1
 8 2023-01-30 date_onset         2
 9 2023-01-31 date_onset         2
10 2023-02-01 date_onset         1
# ℹ 222 more rows

Furthermore, with the incidence2 package, you can specify the desired interval and categorize cases by one or more factors. Below is a code snippet demonstrating weekly cases grouped by the date of onset and gender.

R 

# Group incidence data by week, accounting for sex and case type
weekly_incidence <- incidence2::incidence(
  sim_data,
  date_index = "date_onset",
  interval = "week", # Aggregate by weekly intervals
  groups = c("sex", "case_type") # Group by sex and case type
)

# View the incidence data
weekly_incidence

OUTPUT 

# incidence:  202 x 5
# count vars: date_onset
# groups:     sex, case_type
   date_index sex   case_type count_variable count
   <isowk>    <chr> <chr>     <chr>          <int>
 1 2022-W52   f     probable  date_onset         1
 2 2023-W02   f     probable  date_onset         1
 3 2023-W03   m     suspected date_onset         1
 4 2023-W04   f     probable  date_onset         1
 5 2023-W04   m     confirmed date_onset         2
 6 2023-W04   m     probable  date_onset         1
 7 2023-W04   m     suspected date_onset         1
 8 2023-W05   f     confirmed date_onset         4
 9 2023-W05   f     probable  date_onset         2
10 2023-W05   f     suspected date_onset         2
# ℹ 192 more rows

Dates Completion

When cases are grouped by different factors, it’s possible that these groups may have different date ranges in the resulting incidence2 object. The incidence2 package provides a function called complete_dates() to ensure that an incidence object has the same range of dates for each group. By default, missing counts will be filled with 0.

This functionality is also available as an argument within incidence2::incidence() adding complete_dates = TRUE.

R 

# Create an incidence object grouped by sex, aggregating daily
dialy_incidence_2 <- incidence2::incidence(
  sim_data,
  date_index = "date_onset",
  groups = "sex",
  interval = "day", # Aggregate by daily intervals
  complete_dates = TRUE # Complete missing dates in the incidence object
)

Challenge 1: Can you do it?

  • Task: Aggregate sim_data linelist based on admission date and case outcome in biweekly intervals, and save the results in an object called biweekly_incidence.

Visualization

The incidence2 object can be visualized using the plot() function from the base R package. The resulting graph is referred to as an epidemic curve, or epi-curve for short. The following code snippets generate epi-curves for the dialy_incidence and weekly_incidence incidence objects mentioned above.

R 

# Plot daily incidence data
base::plot(dialy_incidence) +
  ggplot2::labs(
    x = "Time (in days)", # x-axis label
    y = "Dialy cases" # y-axis label
  ) +
  tracetheme::theme_trace() # Apply the custom trace theme

R 

# Plot weekly incidence data
base::plot(weekly_incidence) +
  ggplot2::labs(
    x = "Time (in weeks)", # x-axis label
    y = "weekly cases" # y-axis label
  ) +
  tracetheme::theme_trace() # Apply the custom trace theme

easy aesthetics

We invite you to skim the incidence2 package “Get started” vignette. Find how you can use arguments within plot() to provide aesthetics to your incidence2 class objects!

R 

base::plot(weekly_incidence, fill = "sex")

Some of them include show_cases = TRUE, angle = 45, and n_breaks = 5. Give them a try!

Challenge 2: Can you do it?

  • Task: Visualize biweekly_incidence object.

Curve of cumulative cases

The cumulative number of cases can be calculated using the cumulate() function from an incidence2 object and visualized, as in the example below.

R 

# Calculate cumulative incidence
cum_df <- incidence2::cumulate(dialy_incidence)

# Plot cumulative incidence data using ggplot2
base::plot(cum_df) +
  ggplot2::labs(
    x = "Time (in days)", # x-axis label
    y = "weekly cases" # y-axis label
  ) +
  tracetheme::theme_trace() # Apply the custom trace theme

Note that this function preserves grouping, i.e., if the incidence2 object contains groups, it will accumulate the cases accordingly.

Challenge 3: Can you do it?

  • Task: Visulaize the cumulatie cases from biweekly_incidence object.

Peak estimation

One can estimate the peak –the time with the highest number of recorded cases– using the estimate_peak() function from the {incidence2} package. This function employs a bootstrapping method to determine the peak time.

R 

# Estimate the peak of the daily incidence data
peak <- incidence2::estimate_peak(
  dialy_incidence,
  n = 100,         # Number of simulations for the peak estimation
  alpha = 0.05,    # Significance level for the confidence interval
  first_only = TRUE, # Return only the first peak found
  progress = FALSE  # Disable progress messages
)

# Display the estimated peak
print(peak)

OUTPUT 

# A tibble: 1 × 7
  count_variable observed_peak observed_count bootstrap_peaks lower_ci
  <chr>          <date>                 <int> <list>          <date>
1 date_onset     2023-05-01                22 <df [100 × 1]>  2023-03-26
# ℹ 2 more variables: median <date>, upper_ci <date>

This example demonstrates how to estimate the peak time using the estimate_peak() function at \(95%\) confidence interval and using 100 bootstrap samples.

Challenge 4: Can you do it?

  • Task: Estimate the peak time from biweekly_incidence object.

Visualization with ggplot2

incidence2 produces basic plots for epicurves, but additional work is required to create well-annotated graphs. However, using the ggplot2 package, you can generate more sophisticated and better-annotated epicurves. ggplot2 is a comprehensive package with many functionalities. However, we will focus on three key elements for producing epicurves: histogram plots, scaling date axes and their labels, and general plot theme annotation. The example below demonstrates how to configure these three elements for a simple incidence2 object.

R 

# Define date breaks for the x-axis
breaks <- seq.Date(
  from = min(as.Date(dialy_incidence$date_index, na.rm = TRUE)),
  to = max(as.Date(dialy_incidence$date_index, na.rm = TRUE)),
  by = 20 # every 20 days
)

# Create the plot
ggplot2::ggplot(data = dialy_incidence) +
  geom_histogram(
    mapping = aes(
      x = as.Date(date_index),
      y = count
    ),
    stat = "identity",
    color = "blue", # bar border color
    fill = "lightblue", # bar fill color
    width = 1 # bar width
  ) +
  theme_minimal() + # apply a minimal theme for clean visuals
  theme(
    plot.title = element_text(face = "bold",
                              hjust = 0.5), # center and bold title
    plot.subtitle = element_text(hjust = 0.5), # center subtitle
    plot.caption = element_text(face = "italic",
                                hjust = 0), # italicized caption
    axis.title = element_text(face = "bold"), # bold axis titles
    axis.text.x = element_text(angle = 45, vjust = 0.5) # rotated x-axis text
  ) +
  labs(
    x = "Date", # x-axis label
    y = "Number of cases", # y-axis label
    title = "Daily Outbreak Cases", # plot title
    subtitle = "Epidemiological Data for the Outbreak", # plot subtitle
    caption = "Data Source: Simulated Data" # plot caption
  ) +
  scale_x_date(
    breaks = breaks, # set custom breaks on the x-axis
    labels = scales::label_date_short() # shortened date labels
  )

WARNING 

Warning in geom_histogram(mapping = aes(x = as.Date(date_index), y = count), :
Ignoring unknown parameters: `binwidth`, `bins`, and `pad`

Use the group option in the mapping function to visualize an epicurve with different groups. If there is more than one grouping factor, use the facet_wrap() option, as demonstrated in the example below:

R 

# Plot daily incidence by sex with facets
ggplot2::ggplot(data = dialy_incidence_2) +
  geom_histogram(
    mapping = aes(
      x = as.Date(date_index),
      y = count,
      group = sex,
      fill = sex
    ),
    stat = "identity"
  ) +
  theme_minimal() + # apply minimal theme
  theme(
    plot.title = element_text(face = "bold",
                              hjust = 0.5), # bold and center the title
    plot.subtitle = element_text(hjust = 0.5), # center the subtitle
    plot.caption = element_text(face = "italic", hjust = 0), # italic caption
    axis.title = element_text(face = "bold"), # bold axis labels
    axis.text.x = element_text(angle = 45,
                               vjust = 0.5) # rotate x-axis text for readability
  ) +
  labs(
    x = "Date", # x-axis label
    y = "Number of cases", # y-axis label
    title = "Daily Outbreak Cases by Sex", # plot title
    subtitle = "Incidence of Cases Grouped by Sex", # plot subtitle
    caption = "Data Source: Simulated Data" # caption for additional context
  ) +
  facet_wrap(~sex) + # create separate panels by sex
  scale_x_date(
    breaks = breaks, # set custom date breaks
    labels = scales::label_date_short() # short date format for x-axis labels
  ) +
  scale_fill_manual(values = c("lightblue",
                               "lightpink")) # custom fill colors for sex

WARNING 

Warning in geom_histogram(mapping = aes(x = as.Date(date_index), y = count, :
Ignoring unknown parameters: `binwidth`, `bins`, and `pad`

Challenge 5: Can you do it?

  • Task: Produce an annotated figure for biweekly_incidence using ggplot2 package.

Key Points

  • Use {simulist} package to generate synthetic outbreak data
  • Use incidence2 package to aggregate case data based on a date event, and produce epidemic curves.
  • Use ggplot2 package to produce better annotated epicurves.