Nothing
README.md
simulist: Simulate line list data 
{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.
Key features
{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
Installation
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"))
Quick start
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 NA
However, 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' function
To 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 26.4
In 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 NA
To 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_followup
If 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_followup
Help
To report a bug please open an issue.
Contribute
Contributions to {simulist} are welcomed. Please follow the package
contributing
guide.
Code of Conduct
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.
Citing this package
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/},
#> }
Complimentary R packages
:package: :left_right_arrow: :package: {epiparameter} :package: :left_right_arrow: :package: {epicontacts} :package: :left_right_arrow: :package: {incidence2} :package: :left_right_arrow: :package: {cleanepi}
Related projects
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 functiongen_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 functionmsf_dict().{EpiNow}- a now deprecated R package - includes thesimulate_cases()andgenerate_pseudo_linelist()functions for generating line list data.- generative-nowcasting is a set of R scripts and functions to perform epidemiological nowcasting. There are functions to simulate line list data within the repository, but the repository is not (and does not contain) an R package. Functions can be sourced. Cases are simulated with a renewal process and the simulation can incorporate epidemiological delays and ascertainment.
| | {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 distributions[^1] | :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 size[^2] | :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 maintained[^3] | :white_check_mark: | :x: | :x: | :x: | :x: | :white_check_mark: | | On CRAN | :white_check_mark: | :x: | :x: | :x: | :x: | NA | | Unit testing[^4] | :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.
[^1]: 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.
[^2]: 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.
[^3]: We define Actively maintained as the repository having a commit to the main branch within the last 12 months.
[^4]: Unit testing is ticked if the package contains any form of testing, this can use any testing framework, for example {testthat} or {tinytest}.
Try the simulist package in your browser
Any scripts or data that you put into this service are public.
