{simulist} is an R package to simulate individual-level
infectious disease outbreak data, including line lists and contact
tracing data. It can often be useful to have synthetic datasets like
these available when demonstrating outbreak analytics techniques or
testing new analysis methods.
{simulist} is developed at the Centre
for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical
Medicine as part of Epiverse-TRACE.
{simulist} allows you to simulate realistic line list
and contact tracing data, with:
:hourglass_flowing_sand: Parameterised epidemiological delay
distributions
:hospital: Population-wide or age-stratified
hospitalisation and death risks
:bar_chart: Uniform or
age-structured populations
:chart_with_upwards_trend: Constant or
time-varying case fatality risk
:clipboard: Customisable
probability of case types and contact tracing follow-up
Post-process simulated line list data for:
:date: Real-time outbreak snapshots with right-truncation
:memo:
Messy data with inconsistencies, mistakes and missing values
The package can be installed from CRAN using
install.packages("simulist")You can install the development version of {simulist}
from GitHub with:
# check whether {pak} is installed
if(!require("pak")) install.packages("pak")
pak::pak("epiverse-trace/simulist")Alternatively, install pre-compiled binaries from the Epiverse TRACE R-universe
install.packages("simulist", repos = c("https://epiverse-trace.r-universe.dev", "https://cloud.r-project.org"))library(simulist)A line list can be simulated by calling sim_linelist().
The function provides sensible defaults to quickly generate a
epidemiologically valid data set.
set.seed(1)
linelist <- sim_linelist()
head(linelist)
#> id case_name case_type sex age date_onset date_reporting
#> 1 1 Lolette Phillips suspected f 59 2023-01-01 2023-01-01
#> 2 2 James Jack suspected m 90 2023-01-01 2023-01-01
#> 3 3 Chen Kantha confirmed m 4 2023-01-02 2023-01-02
#> 4 5 Saleema al-Zaki probable f 29 2023-01-04 2023-01-04
#> 5 6 David Ponzio confirmed m 14 2023-01-05 2023-01-05
#> 6 7 Christopher Ward probable m 85 2023-01-06 2023-01-06
#> date_admission outcome date_outcome date_first_contact date_last_contact
#> 1 2023-01-09 died 2023-01-13 <NA> <NA>
#> 2 <NA> recovered <NA> 2022-12-29 2023-01-03
#> 3 <NA> recovered <NA> 2022-12-28 2023-01-01
#> 4 <NA> recovered <NA> 2022-12-28 2023-01-04
#> 5 2023-01-09 died 2023-01-23 2022-12-31 2023-01-04
#> 6 2023-01-08 recovered <NA> 2022-12-31 2023-01-06
#> ct_value
#> 1 NA
#> 2 NA
#> 3 24.8
#> 4 NA
#> 5 24.6
#> 6 NAHowever, to simulate a more realistic line list using epidemiological
parameters estimated for an infectious disease outbreak we can use
previously estimated epidemiological parameters. These can be from the
{epiparameter} R package if available, or if these are not
in the {epiparameter} database yet (such as the contact
distribution for COVID-19) we can define them ourselves. Here we define
a contact distribution, period of infectiousness,
onset-to-hospitalisation delay, and onset-to-death delay.
library(epiparameter)# create COVID-19 contact distribution
contact_distribution <- epiparameter::epiparameter(
disease = "COVID-19",
epi_name = "contact distribution",
prob_distribution = create_prob_distribution(
prob_distribution = "pois",
prob_distribution_params = c(mean = 2)
)
)
#> Citation cannot be created as author, year, journal or title is missing
# create COVID-19 infectious period
infectious_period <- epiparameter::epiparameter(
disease = "COVID-19",
epi_name = "infectious period",
prob_distribution = create_prob_distribution(
prob_distribution = "gamma",
prob_distribution_params = c(shape = 1, scale = 1)
)
)
#> Citation cannot be created as author, year, journal or title is missing
# create COVID-19 onset to hospital admission
onset_to_hosp <- epiparameter(
disease = "COVID-19",
epi_name = "onset to hospitalisation",
prob_distribution = create_prob_distribution(
prob_distribution = "lnorm",
prob_distribution_params = c(meanlog = 1, sdlog = 0.5)
)
)
#> Citation cannot be created as author, year, journal or title is missing
# get onset to death from {epiparameter} database
onset_to_death <- epiparameter::epiparameter_db(
disease = "COVID-19",
epi_name = "onset to death",
single_epiparameter = TRUE
)
#> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan
#> B, Kinoshita R, Nishiura H (2020). "Incubation Period and Other
#> Epidemiological Characteristics of 2019 Novel Coronavirus Infections
#> with Right Truncation: A Statistical Analysis of Publicly Available
#> Case Data." _Journal of Clinical Medicine_. doi:10.3390/jcm9020538
#> <https://doi.org/10.3390/jcm9020538>..
#> To retrieve the citation use the 'get_citation' functionTo simulate a line list for COVID-19 with an Poisson contact
distribution with a mean number of contacts of 2 and a probability of
infection per contact of 0.5, we use the sim_linelist()
function. The mean number of contacts and probability of infection
determine the outbreak reproduction number, if the resulting
reproduction number is around one it means we will likely get a
reasonably sized outbreak (10 - 1,000 cases, varying due to the
stochastic simulation).
Warning: the reproduction number of the
simulation results from the contact distribution
(contact_distribution) and the probability of infection
(prob_infection); the number of infections is a binomial
sample of the number of contacts for each case with the probability of
infection (i.e. being sampled) given by prob_infection. If
the average number of secondary infections from each primary case is
greater than 1 then this can lead to the outbreak becoming extremely
large. There is currently no depletion of susceptible individuals in the
simulation model, so the maximum outbreak size (second element of the
vector supplied to the outbreak_size argument) can be used
to return a line list early without producing an excessively large data
set.
set.seed(1)
linelist <- sim_linelist(
contact_distribution = contact_distribution,
infectious_period = infectious_period,
prob_infection = 0.5,
onset_to_hosp = onset_to_hosp,
onset_to_death = onset_to_death
)
head(linelist)
#> id case_name case_type sex age date_onset date_reporting
#> 1 1 Kevin Pullen suspected m 1 2023-01-01 2023-01-01
#> 2 2 Carisa Flores-Gonzalez confirmed f 29 2023-01-01 2023-01-01
#> 3 3 Maazin el-Othman confirmed m 78 2023-01-01 2023-01-01
#> 4 5 Faisal el-Vaziri suspected m 70 2023-01-01 2023-01-01
#> 5 6 Lynsey Duron confirmed f 28 2023-01-01 2023-01-01
#> 6 8 Lilibeth Black confirmed f 61 2023-01-01 2023-01-01
#> date_admission outcome date_outcome date_first_contact date_last_contact
#> 1 2023-01-03 died 2023-01-18 <NA> <NA>
#> 2 2023-01-03 died 2023-02-09 2022-12-30 2023-01-08
#> 3 <NA> recovered <NA> 2022-12-31 2023-01-05
#> 4 2023-01-04 recovered <NA> 2022-12-31 2023-01-04
#> 5 2023-01-05 recovered <NA> 2022-12-29 2023-01-02
#> 6 <NA> recovered <NA> 2022-12-28 2023-01-05
#> ct_value
#> 1 NA
#> 2 25.8
#> 3 24.9
#> 4 NA
#> 5 24.5
#> 6 26.4In this example, the line list is simulated using the default values
(see ?sim_linelist). The default hospitalisation risk is
assumed to be 0.2 (i.e. there is a 20% probability an infected
individual becomes hospitalised) and the start date of the outbreak is
1st January 2023. To modify either of these, we can specify them in the
function.
linelist <- sim_linelist(
contact_distribution = contact_distribution,
infectious_period = infectious_period,
prob_infection = 0.5,
onset_to_hosp = onset_to_hosp,
onset_to_death = onset_to_death,
hosp_risk = 0.01,
outbreak_start_date = as.Date("2019-12-01")
)
head(linelist)
#> id case_name case_type sex age date_onset date_reporting
#> 1 1 Kacy Kim suspected f 80 2019-12-01 2019-12-01
#> 2 2 Jina Warnes probable f 85 2019-12-01 2019-12-01
#> 3 4 Raadi el-Yasin probable m 76 2019-12-01 2019-12-01
#> 4 8 Joshua Castaneda confirmed m 12 2019-12-01 2019-12-01
#> 5 11 Fat'hiyaa al-Zafar suspected f 50 2019-12-01 2019-12-01
#> 6 14 Matthew Sheldon probable m 54 2019-12-01 2019-12-01
#> date_admission outcome date_outcome date_first_contact date_last_contact
#> 1 <NA> recovered <NA> <NA> <NA>
#> 2 <NA> recovered <NA> 2019-11-29 2019-12-05
#> 3 <NA> recovered <NA> 2019-11-29 2019-12-08
#> 4 <NA> died 2019-12-17 2019-11-26 2019-12-05
#> 5 <NA> recovered <NA> 2019-11-28 2019-12-01
#> 6 <NA> recovered <NA> 2019-11-25 2019-12-01
#> ct_value
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 23.7
#> 5 NA
#> 6 NATo simulate a table of contacts of cases (i.e. to reflect a contact tracing dataset) we can use the same parameters defined for the example above.
contacts <- sim_contacts(
contact_distribution = contact_distribution,
infectious_period = infectious_period,
prob_infection = 0.5
)
head(contacts)
#> from to age sex date_first_contact
#> 1 Rodrigo Deluca Jeremiah Sitinjak 23 m 2023-01-01
#> 2 Rodrigo Deluca Eric Green 16 m 2022-12-30
#> 3 Rodrigo Deluca Skye Chee 40 f 2022-12-30
#> 4 Rodrigo Deluca Samantha Parga 20 f 2022-12-27
#> 5 Rodrigo Deluca Abdul Rauf al-Mirza 4 m 2022-12-28
#> 6 Jeremiah Sitinjak Habsa Huntington 9 f 2022-12-29
#> date_last_contact was_case status
#> 1 2023-01-04 Y case
#> 2 2023-01-02 Y case
#> 3 2023-01-02 N under_followup
#> 4 2023-01-02 Y case
#> 5 2023-01-02 Y case
#> 6 2023-01-03 N under_followupIf both the line list and contacts table are required, they can be
jointly simulated using the sim_outbreak() function. This
uses the same inputs as sim_linelist() and
sim_contacts() to produce a line list and contacts table of
the same outbreak (the arguments also have the same default settings as
the other functions).
outbreak <- sim_outbreak(
contact_distribution = contact_distribution,
infectious_period = infectious_period,
prob_infection = 0.5,
onset_to_hosp = onset_to_hosp,
onset_to_death = onset_to_death
)
head(outbreak$linelist)
#> id case_name case_type sex age date_onset date_reporting
#> 1 1 Joshua Lymburn probable m 45 2023-01-01 2023-01-01
#> 2 2 Augustine Gonzales confirmed m 9 2023-01-02 2023-01-02
#> 3 4 Takeya Searles suspected f 35 2023-01-02 2023-01-02
#> 4 6 Luke Flood confirmed m 4 2023-01-02 2023-01-02
#> 5 8 Allison Fage-Armstrong probable f 2 2023-01-02 2023-01-02
#> 6 10 Faai Z el-Safar probable m 48 2023-01-02 2023-01-02
#> date_admission outcome date_outcome date_first_contact date_last_contact
#> 1 <NA> recovered <NA> <NA> <NA>
#> 2 <NA> recovered <NA> 2023-01-01 2023-01-05
#> 3 <NA> recovered <NA> 2022-12-31 2023-01-05
#> 4 <NA> recovered <NA> 2023-01-02 2023-01-05
#> 5 <NA> recovered <NA> 2022-12-31 2023-01-05
#> 6 <NA> recovered <NA> 2022-12-29 2023-01-06
#> ct_value
#> 1 NA
#> 2 24.6
#> 3 NA
#> 4 25.7
#> 5 NA
#> 6 NA
head(outbreak$contacts)
#> from to age sex date_first_contact
#> 1 Joshua Lymburn Augustine Gonzales 9 m 2023-01-01
#> 2 Joshua Lymburn Cecilia Cortez 81 f 2022-12-27
#> 3 Joshua Lymburn Takeya Searles 35 f 2022-12-31
#> 4 Augustine Gonzales Thorsen Stewart 75 m 2022-12-31
#> 5 Augustine Gonzales Luke Flood 4 m 2023-01-02
#> 6 Augustine Gonzales Suki Lang 15 f 2022-12-29
#> date_last_contact was_case status
#> 1 2023-01-05 Y case
#> 2 2023-01-03 N under_followup
#> 3 2023-01-05 Y case
#> 4 2023-01-06 N under_followup
#> 5 2023-01-05 Y case
#> 6 2023-01-03 N under_followupTo report a bug please open an issue.
Contributions to {simulist} are welcomed. Please follow
the package
contributing guide.
Please note that the {simulist} project is released with
a Contributor
Code of Conduct. By contributing to this project, you agree to abide
by its terms.
citation("simulist")
#> To cite package 'simulist' in publications use:
#>
#> Lambert J, Tamayo C (2025). _simulist: Simulate Disease Outbreak Line
#> List and Contacts Data_. doi:10.5281/zenodo.10471458
#> <https://doi.org/10.5281/zenodo.10471458>,
#> <https://epiverse-trace.github.io/simulist/>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {simulist: Simulate Disease Outbreak Line List and Contacts Data},
#> author = {Joshua W. Lambert and Carmen Tamayo},
#> year = {2025},
#> doi = {10.5281/zenodo.10471458},
#> url = {https://epiverse-trace.github.io/simulist/},
#> }:package: :left_right_arrow: :package: {epiparameter}
:package: :left_right_arrow: :package: {epicontacts}
:package: :left_right_arrow: :package: {incidence2}
:package: :left_right_arrow: :package: {cleanepi}
This project has some overlap with other R packages. Here we list these packages and provide a table of features and attributes that are present for each package to help decide which package is appropriate for each use-case.
In some cases the packages are dedicated to simulating line list and other epidemiological data (e.g. {simulist}), in others the line list simulation is one part of a wider R package (e.g. {EpiNow}).
{LLsim}
simulates line list data using a stochastic SIR model with a fixed
population with observation and reporting delays. Line list data is
generated in two steps, 1) the SIR model simulates the outbreak
(simpleSim()), 2) the outbreak data is converted into a
line list (createLineList()).{simulacr}
uses a branching process model to simulate cases and contacts for an
outbreak. It simulates transmission of infections using other
epidemiological R packages ({epicontacts} and
{distcrete}) to parameterise and plot simulated data.{epidict} is a
package that can be used to simulate outbreak data, including line
lists, in a DHIS2 format, and survey data that mimics the format by
Kobo, using the function gen_data(). In addition, MSF
outbreak data are available in this package as data dictionaries for
Acute Jaundice Syndrome, Cholera, Measles and Meningitis, accessible
through the function msf_dict().{EpiNow}
- a now deprecated R package - includes the
simulate_cases() and
generate_pseudo_linelist() functions for generating line
list data.| {simulist} | {LLsim} | {simulacr} | {epidict} | {EpiNow} | generative-nowcasting | |
|---|---|---|---|---|---|---|
| Simulates line list | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| Simulates contacts | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: | :x: |
| Parameterised with epi distributions1 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :white_check_mark: | :white_check_mark: |
| Interoperable with {epicontacts} | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: | :x: |
| Explicit population size2 | :x: | :white_check_mark: | :white_check_mark: | :x: | :x: | :x: |
| R package | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: |
| Actively maintained3 | :white_check_mark: | :x: | :x: | :x: | :x: | :white_check_mark: |
| On CRAN | :white_check_mark: | :x: | :x: | :x: | :x: | NA |
| Unit testing4 | :white_check_mark: | :white_check_mark: | :x: | :white_check_mark: | :x: | NA |
If there is another package with this functionality missing from the list that should be added, or if a package included in this list has been updated and the table should reflect this please contribute by making an issue or a pull request.
Some packages are related to {simulist} but do not simulate line list data. These include:
{outbreaks}
an R package containing a library of outbreak data sets, including line
list data, for a variety of past and simulated outbreaks, e.g. Ebola and
MERS.{ringbp}
an R package to simulate cases using an individual-level transmission
model with contact tracing.{epichains}
an R package with functionality to simulate transmission chains using a
branching process model.The {outbreaks} package is useful if data from a past outbreak data or generic line list data is required. The {ringbp} and {epichains} packages can be used to generate case data over time which can then be converted into a line list with some manual post-processing.
Another package for creating messy data is the {messy} package.
This can be used, either independently or in combination with
messy_linelist(), to create messy line list and contacts
data.
In this context Parameterised with epi distributions means that the simulation uses epidemiological distributions (e.g. serial interval, infectious period) to parameterise the model and the parameters of these epi distributions can be modified by the user.↩︎
Explicit population size refers to the simulation using a finite population size which is controlled by the user for the depletion of susceptible individuals in the model.↩︎
We define Actively maintained as the repository having a commit to the main branch within the last 12 months.↩︎
Unit testing is ticked if the package contains any form of testing, this can use any testing framework, for example {testthat} or {tinytest}.↩︎