Simulate Disease Outbreak Line List and Contacts Data • simulist

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:

⏳ Parameterised epidemiological delay distributions
🏥 Population-wide or age-stratified hospitalisation and death risks
📊 Uniform or age-structured populations
📈 Constant or time-varying case fatality risk
📋 Customisable probability of case types and contact tracing follow-up

Installation

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")

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_admission
#> 1  1    Marquione Currington suspected   m  59 2023-01-01     2023-01-09
#> 2  2      Ghaaliba el-Hassen  probable   f  90 2023-01-01           <NA>
#> 3  3 Leslie Morales-Gonzalez  probable   f   4 2023-01-02           <NA>
#> 4  5        Labeeb el-Hariri confirmed   m  29 2023-01-04           <NA>
#> 5  6             Carla Moore confirmed   f  14 2023-01-05     2023-01-09
#> 6  7      Saabiqa al-Hammoud  probable   f  85 2023-01-06     2023-01-08
#>     outcome date_outcome date_first_contact date_last_contact ct_value
#> 1      died   2023-01-13               <NA>              <NA>       NA
#> 2 recovered         <NA>         2022-12-31        2023-01-05       NA
#> 3 recovered         <NA>         2022-12-30        2023-01-01       NA
#> 4 recovered         <NA>         2023-01-05        2023-01-05     24.5
#> 5      died   2023-01-23         2023-01-07        2023-01-08     24.5
#> 6 recovered         <NA>         2023-01-03        2023-01-06       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::epidist(
  disease = "COVID-19", 
  epi_dist = "contact 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::epidist(
  disease = "COVID-19",
  epi_dist = "infectious period",
  prob_distribution = "gamma",
  prob_distribution_params = c(shape = 1, scale = 1)
)
#> Citation cannot be created as author, year, journal or title is missing

# get onset to hospital admission from {epiparameter} database
onset_to_hosp <- epiparameter::epidist_db(
  disease = "COVID-19",
  epi_dist = "onset to hospitalisation",
  single_epidist = 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

# get onset to death from {epiparameter} database
onset_to_death <- epiparameter::epidist_db(
  disease = "COVID-19",
  epi_dist = "onset to death",
  single_epidist = 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_admission   outcome
#> 1  1 Wajdi al-Demian  probable   m  35 2023-01-01           <NA> recovered
#> 2  2   Raaid el-Diab confirmed   m  43 2023-01-01     2023-01-07 recovered
#> 3  3  Nickolas Nault suspected   m   1 2023-01-01           <NA> recovered
#> 4  5     Hee Kennedy confirmed   m  78 2023-01-01     2023-01-03      died
#> 5  6     Hope Arshad suspected   f  22 2023-01-01     2023-01-28      died
#> 6  8  Shanta Holiday  probable   f  28 2023-01-01           <NA> recovered
#>   date_outcome date_first_contact date_last_contact ct_value
#> 1         <NA>               <NA>              <NA>       NA
#> 2         <NA>         2022-12-30        2023-01-05     24.1
#> 3         <NA>         2022-12-30        2023-01-02       NA
#> 4   2023-01-21         2022-12-29        2023-01-02     24.1
#> 5   2023-01-10         2023-01-01        2023-01-03       NA
#> 6         <NA>         2023-01-03        2023-01-04       NA

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_admission   outcome
#> 1  1 Kristiana Acheampong confirmed   f  88 2019-12-01           <NA> recovered
#> 2  3   Jadeeda el-Abdalla confirmed   f   8 2019-12-01           <NA> recovered
#> 3  4     Dominic Sandoval  probable   m  48 2019-12-01           <NA> recovered
#> 4  5          Zoe Johnson confirmed   f   3 2019-12-01           <NA> recovered
#> 5  6        Breann Bruski  probable   f  25 2019-12-01           <NA> recovered
#> 6  7       Joseph Charley suspected   m  57 2019-12-01           <NA> recovered
#>   date_outcome date_first_contact date_last_contact ct_value
#> 1         <NA>               <NA>              <NA>     26.7
#> 2         <NA>         2019-11-30        2019-12-02     26.7
#> 3         <NA>         2019-11-30        2019-12-03       NA
#> 4         <NA>         2019-12-02        2019-12-04     26.7
#> 5         <NA>         2019-11-28        2019-12-04       NA
#> 6         <NA>         2019-12-05        2019-12-06       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 Munisa el-Kamal      Alicia Topper  76   f         2023-01-05
#> 2 Munisa el-Kamal     Donald Ramirez  24   m         2023-01-01
#> 3 Munisa el-Kamal   Brittney Jarmond   7   f         2023-01-05
#> 4 Munisa el-Kamal Ramalaan el-Saadeh  57   m         2023-01-01
#> 5   Alicia Topper         Cody Cohen   7   m         2023-01-02
#> 6   Alicia Topper        Ashley Kohl  48   f         2022-12-31
#>   date_last_contact was_case         status
#> 1        2023-01-07        Y           case
#> 2        2023-01-03        Y           case
#> 3        2023-01-05        N under_followup
#> 4        2023-01-04        N        unknown
#> 5        2023-01-04        Y           case
#> 6        2023-01-03        Y           case

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$linelist)
#>   id         case_name case_type sex age date_onset date_admission   outcome
#> 1  1     Okatomi Reish confirmed   f  30 2023-01-01           <NA> recovered
#> 2  6   Angel Escalante confirmed   m  83 2023-01-01           <NA> recovered
#> 3  8  Miqdaad el-Turay  probable   m  17 2023-01-01           <NA> recovered
#> 4  9 Jennifer Gonzalez confirmed   f  74 2023-01-01           <NA>      died
#> 5 10        Brendan Wu confirmed   m  33 2023-01-01     2023-01-03 recovered
#> 6 12   Erminio Mandell  probable   m  24 2023-01-01           <NA> recovered
#>   date_outcome date_first_contact date_last_contact ct_value
#> 1         <NA>               <NA>              <NA>     23.9
#> 2         <NA>         2023-01-01        2023-01-05     23.9
#> 3         <NA>         2022-12-31        2023-01-02       NA
#> 4   2023-01-21         2023-01-07        2023-01-08     23.9
#> 5         <NA>         2023-01-01        2023-01-01     23.9
#> 6         <NA>         2023-01-02        2023-01-05       NA
head(outbreak$contacts)
#>              from                 to age sex date_first_contact
#> 1   Okatomi Reish         Jesse Lynn  44   f         2023-01-04
#> 2   Okatomi Reish        Joshua Rose  89   m         2023-01-05
#> 3   Okatomi Reish   Wadee'a al-Rayes  11   f         2023-01-04
#> 4   Okatomi Reish  Khaleel al-Hamady  19   m         2022-12-30
#> 5   Okatomi Reish    Angel Escalante  83   m         2023-01-01
#> 6 Angel Escalante Burhaan el-Hashemi  87   m         2022-12-29
#>   date_last_contact was_case           status
#> 1        2023-01-05        N lost_to_followup
#> 2        2023-01-05        N          unknown
#> 3        2023-01-05        N   under_followup
#> 4        2023-01-03        N   under_followup
#> 5        2023-01-05        Y             case
#> 6        2023-01-02        N          unknown

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 (2024). _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 = {2024},
#>     doi = {10.5281/zenodo.10471458},
#>     url = {https://epiverse-trace.github.io/simulist/},
#>   }

Complimentary R packages

📦 ↔︎️ 📦 {epiparameter}
📦 ↔︎️ 📦 {epicontacts}
📦 ↔︎️ 📦 {incidence2}

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.
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.

Table of line list simulator features

	{simulist}	{LLsim}	{simulacr}	{epidict}	{EpiNow}	generative-nowcasting
Simulates line list	✅	✅	✅	✅	✅	✅
Simulates contacts	✅	✅	✅	❌	❌	❌
Parameterised with epi distributions¹	✅	✅	✅	❌	✅	✅
Interoperable with {epicontacts}	✅	✅	✅	❌	❌	❌
Explicit population size²	❌	✅	✅	❌	❌	❌
R package	✅	✅	✅	✅	✅	❌
Actively maintained³	✅	❌	❌	❌	❌	✅
On CRAN	❌	❌	❌	❌	❌	NA
Unit testing⁴	✅	✅	❌	✅	❌	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.

simulist: Simulate line list data