Aggregate and visulaize
Last updated on 2024-04-29 | 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 outbreaks and epidemic data, and how
to achieved that using a couples of handy R packages. A key
observation in EDA of epidemic analysis is capturing the relationship
between time and the number of reported cases, spanning various
categories (confirmed, hospitalized, deaths, and recoveries), locations,
and other demographic factors such as gender, age, etc.
Synthetic outbreak data
To illustrate the process of conducting EDA on outbreak data, we will
generate a line list for a hypothetical Ebola outbreak utilizing the
{simulist} package. This line list dataset offers
individual-level information about the outbreak. For our simulation, we
will assume that the dynamics of this outbreak are influenced by several
factors: the contact distribution (average number of contacts for an
infected case), distribution of contact intervals (time period between
contacts), and the delay distributions of onset to hospitalization and
onset to death. These latter distributions can be sourced from
literature and are conveniently available in the
{epiparameter} package, see the below code chunk.
R
# Load simulist and epiparameter packages
library("simulist")
library("epiparameter")
# Define contact distribution
contact_dist <- epiparameter::epidist(
disease = "Ebola",
epi_dist = "contact distribution",
prob_distribution = "pois",
prob_distribution_params = c(mean = 2)
)
# Define distribution for interval between contact
cont_interval <- epiparameter::epidist(
disease = "Ebola",
epi_dist = "contact interval",
prob_distribution = "gamma",
prob_distribution_params = c(shape = 1, scale = 1)
)
# Define onset to hospitalized distribution
onset_to_hosp <- contact_dist <- epiparameter::epidist(
disease = "Ebola",
epi_dist = "onset to hospitalisatio",
prob_distribution = "pois",
prob_distribution_params = c(mean = 7)
)
# get onset to death from {epiparameter} database
onset_to_death <- epiparameter::epidist_db(
disease = "Ebola",
epi_dist = "onset to death",
single_epidist = TRUE
)
# Define distribution for infectious period
infect_period <- epiparameter::epidist(
disease = "Ebola",
epi_dist = "Infectious period",
prob_distribution = "gamma",
prob_distribution_params = c(shape = 1, scale = 1)
)
Additionally, we assume that the outbreak started at the beginning of 2023, is highly contagious with a probability of infection of \(80\%\), and its minimum and maximum sizes are 1000 and 10,000, respectively. Combining these assumptions with the mentioned distributions, the code chunk below generates a simulated line list:
R
# Set seed to 1 to have the same results
base::set.seed(1)
# Generate simulation data using the defined distribution.
linelist <- simulist::sim_linelist(
contact_dist,
infect_period,
prob_infect = 0.6,
onset_to_hosp,
onset_to_death,
hosp_risk = 0.2,
hosp_death_risk = 0.5,
non_hosp_death_risk = 0.05,
outbreak_start_date = as.Date("2023-01-01"),
add_names = TRUE,
add_ct = TRUE,
outbreak_size = c(1000, 10000),
population_age = c(1, 90),
case_type_probs = c(suspected = 0.2, probable = 0.1, confirmed = 0.7),
config = simulist::create_config()
)
# View first few rows of the generated data
utils::head(linelist)
OUTPUT
id case_name case_type sex age date_onset date_admission
1 1 Keegan Hardy-Roy confirmed m 3 2023-01-01 <NA>
2 6 Alexandria Torres-Perez probable f 13 2023-01-01 <NA>
3 7 Mitchell Reinhart confirmed m 74 2023-01-01 2023-01-08
4 9 Stephanie Loadman-Copeland confirmed f 65 2023-01-01 2023-01-10
5 10 Sufyaan al-Irani confirmed m 8 2023-01-02 <NA>
6 11 Rohan Nguyen confirmed m 27 2023-01-01 <NA>
date_death date_first_contact date_last_contact ct_value
1 <NA> <NA> <NA> 24.9
2 <NA> 2022-12-30 2023-01-02 NA
3 <NA> 2022-12-31 2023-01-04 24.9
4 <NA> 2023-01-03 2023-01-04 24.9
5 <NA> 2022-12-31 2023-01-02 24.9
6 <NA> 2023-01-01 2023-01-05 24.9Aggregating
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 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 object from the simulated Ebola
linelist data based on the date of onset.
R
# load incidence2 package
library("incidence2")
# create incidence object by aggregating case data based on the date of onset
dialy_incidence_data <- incidence2::incidence(
linelist,
date_index = "date_onset",
interval = 1
)
# View the first incidence data for the first 5 days
utils::head(dialy_incidence_data, 5)
OUTPUT
# incidence: 5 x 3
# count vars: date_onset
date_index count_variable count
* <period> <chr> <int>
1 2023-01-01 date_onset 475
2 2023-01-02 date_onset 4904
3 2023-01-03 date_onset 5478
4 2023-01-04 date_onset 5319
5 2023-01-05 date_onset 3520Furthermore, 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
# Grouping data by week
weekly_incidence_data <- incidence2::incidence(
linelist,
date_index = "date_onset",
interval = 7,
groups = c("sex", "case_type")
)
# View incidence data for the first 5 weeks
utils::head(weekly_incidence_data, 5)
OUTPUT
# incidence: 5 x 5
# count vars: date_onset
# groups: sex, case_type
date_index sex case_type count_variable count
* <period> <chr> <chr> <chr> <int>
1 2022-12-29 to 2023-01-04 f confirmed date_onset 5576
2 2022-12-29 to 2023-01-04 f probable date_onset 766
3 2022-12-29 to 2023-01-04 f suspected date_onset 1612
4 2022-12-29 to 2023-01-04 m confirmed date_onset 5760
5 2022-12-29 to 2023-01-04 m probable date_onset 787Notes
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.
R
# Create incidence object
dialy_incidence_data_2 <- incidence2::incidence(
linelist,
date_index = "date_onset",
groups = "sex",
interval = 1
)
# Complete missing dates in the incidence object
incidence2::complete_dates(dialy_incidence_data_2,
expand = TRUE,
fill = 0L, by = 1L,
allow_POSIXct = FALSE
)
OUTPUT
# incidence: 24 x 4
# count vars: date_onset
# groups: sex
date_index sex count_variable count
<period> <chr> <chr> <int>
1 2023-01-01 f date_onset 242
2 2023-01-01 m date_onset 233
3 2023-01-02 f date_onset 2403
4 2023-01-02 m date_onset 2501
5 2023-01-03 f date_onset 2709
6 2023-01-03 m date_onset 2769
7 2023-01-04 f date_onset 2600
8 2023-01-04 m date_onset 2719
9 2023-01-05 f date_onset 1763
10 2023-01-05 m date_onset 1757
# ℹ 14 more rowsVisualization
“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_data and weekly_incidence_data
incidence objects mentioned above.”
R
# Load ggplot2 and tracetheme packages
library("ggplot2")
library("tracetheme")
# Plot daily incidence data
base::plot(dialy_incidence_data) + ggplot2::labs(
x = "Time (in days)",
y = "Dialy cases"
) + tracetheme::theme_trace()
R
# Plot weekly incidence data
base::plot(weekly_incidence_data) + ggplot2::labs(
x = "Time (in days)",
y = "weekly cases"
) + tracetheme::theme_trace()
Challenge 1: Can you do it?
- Using suitable distributions for contacts, contact interval, infectious period, onset to hospitalized, and onset to death, generate a simulated linelist data for Marburg outbreak that has the probability of \(0.5\) infection?
- Aggregate the generated linelist and produce some epidemic curves?
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.