Getting Started with {simulist}

This is an introductory vignette to the {simulist} R package. {simulist} simulates two types of common epidemiological data collected during infectious disease outbreaks: 1) a line list, which provides individual-level descriptions of cases in an outbreak; 2) a contact dataset, which provides details of which others individuals were in contact with each of the cases.

The main function in the {simulist} package is sim_linelist(). This functions takes in arguments that control the dynamics of the outbreak, such as the infectious period, and outputs a line list table (<data.frame>) with case information for each infected individual.

For this introduction we will simulate a line list for the early stages of a COVID-19 (SARS-CoV-2) outbreak. This will require two R packages: {simulist}, to produce the line list, and {epiparameter} to provide epidemiological parameters, such as onset-to-death delays.

First we load in some data that is required for the line list simulation. Data on epidemiological parameters and distributions are read from the {epiparameter} R package.

# create contact distribution (not available from {epiparameter} database)
contact_distribution <- 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 infectious period (not available from {epiparameter} database)
infectious_period <- 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

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_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

The seed is set to ensure the output of the vignette is consistent. When using {simulist}, setting the seed is not required unless you need to simulate the same line list multiple times.

The first argument in sim_linelist() is the contact distribution (contact_distribution), which here we specify as Poisson distribution with a mean (average) number of contacts of 2, and with the infectious period and probability of infection per contact (prob_infection) will control the growth rate of the simulated epidemic. Here we set the probability of infection as 0.5 (on average half of contacts become infected). The minimum requirements to simulate a line list are the contact distribution, the infectious period, probability of infection, onset-to-hospitalisation delay and onset-to-death delay.

Controlling outbreak size

The reproduction number ($R$) has a strong influence on the size of an outbreak. For {simulist}, the reproduction number is, not provided directly, but rather is determined by the mean number of contacts and the probability of infection. However, the {simulist} package generates line list data using a stochastic algorithm, so even when $R < 1$ it can produce a substantial outbreak by chance, or an $R >> 1$ will sometimes not produce a vast epidemic in one simulation (i.e. one replicate) due to the stochasticity.

Alert

The reproduction number ($R$) 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_infect. If the average number of secondary infections from each primary case is greater than 1 ($R > 1$) then this can lead to the outbreak becoming extremely large.

There is currently no depletion of susceptible individuals in the simulation model (i.e. infinite population size), 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.

If $R > 1$, the simulation may return early after reaching the maximum outbreak size. In these scenarios when $R > 1$, the $R$ value is controlling the rate at which the maximum outbreak size is reached rather than the size of the outbreak (not all simulations with $R > 1$ will reach the maximum outbreak size due to stochasticity).

The simulation is therefore sensitive to the contact distribution and probability of infection resulting in an $R$ just above or below 1.

When requiring a minimum or maximum outbreak size we can specify the outbreak_size argument in sim_linelist(). By default this is set to 10 and 10,000 for the minimum and maximum, respectively. In the case of the minimum outbreak size, this means that the simulation will not return a line list until the conditioning has been met. In other words, the simulation will resimulate a branching process model until an outbreak infects at least 10 people. In the case of the maximum outbreak size, if the number of infected individuals exceeds the maximum the simulation will end, even if there are still infectious individuals capable of continuing transmission, the function will return the data early with a warning that the number of infections in the data has reached the maximum and stating how many cases and contacts are in the data output.

When requiring a line list that represents a large outbreak, such as the COVID-19 outbreak, setting the outbreak_size to a larger number guarantees a line list of at least that size. Here we simulate a line list requiring at least 250 cases (and fewer than 10,000 cases). The maximum number of cases can also be increased when simulating outbreaks such as global pandemics.

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,
  outbreak_size = c(250, 1e4)
)
head(linelist)
#>   id            case_name case_type sex age date_onset date_reporting
#> 1  1          John Farley confirmed   m  61 2023-01-01     2023-01-01
#> 2  2      Jared Weixelman confirmed   m  29 2023-01-01     2023-01-01
#> 3  3         Kyla Collins confirmed   f  71 2023-01-01     2023-01-01
#> 4  4    Shianne Crawleigh  probable   f  23 2023-01-01     2023-01-01
#> 5  5 Kathryn Pfannenstiel confirmed   f   7 2023-01-01     2023-01-01
#> 6  6     Catherine Morris  probable   f  19 2023-01-01     2023-01-01
#>   date_admission   outcome date_outcome date_first_contact date_last_contact
#> 1           <NA> recovered         <NA>               <NA>              <NA>
#> 2           <NA> recovered         <NA>         2022-12-31        2023-01-09
#> 3     2023-01-04      died   2023-01-13         2022-12-28        2023-01-02
#> 4     2023-01-05 recovered         <NA>         2022-12-29        2023-01-04
#> 5           <NA> recovered         <NA>         2022-12-30        2023-01-08
#> 6     2023-01-04      died   2023-01-20         2022-12-29        2023-01-02
#>   ct_value
#> 1     22.7
#> 2     24.4
#> 3     24.2
#> 4       NA
#> 5     27.0
#> 6       NA

The amount of time the simulation takes can be determined by the mean of the contact distribution (contact_distribution), the probability of infection (prob_infection) and conditioning the outbreak size (outbreak_size). If the minimum outbreak_size is large, for example hundreds or thousands of cases, and the mean number of contacts and probability of infection mean the reproduction number is below one, it will take many branching process simulations until finding one that produces a sufficiently large epidemic.

Case type

During an infectious disease outbreak it may not be possible to confirm every infection as a case. A confirmed case is typically defined via a diagnostic test. There are several reasons why a case may not be confirmed, including limited testing capacity and mild or non-specific early symptoms, especially in fast growing epidemics. We therefore include two other categories for cases: probable and suspected. For example, probable cases may be those that show clinical evidence for the disease but have not, or cannot, be confirmed by a diagnostic test. Suspected cases are those that are possibly infected but do not show clear clinical or epidemiological evidence, nor has a diagnostic test been performed. Hence the distribution of suspected/probable/confirmed will depend on the pathogen characteristics, outbreak-specific definitions, and resources available.

The line list output from the {simulist} simulation contains a column (case_type) with the type of case.

{simulist} can simulate varying probabilities of each case being suspected, probable or confirmed. By default the sim_linelist() function uses probabilities of suspected = 0.2, probable = 0.3 and confirmed = 0.5.

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 Hannelore Butterworth  probable   f  41 2023-01-01     2023-01-01
#> 2  3      Daniel Schenally confirmed   m   9 2023-01-01     2023-01-01
#> 3  4     Margaret Vanovski confirmed   f  18 2023-01-01     2023-01-01
#> 4  5        Belle Castillo confirmed   f  53 2023-01-01     2023-01-01
#> 5  7           Julio Perez confirmed   m  58 2023-01-02     2023-01-02
#> 6  9       Victor Martinez suspected   m  69 2023-01-01     2023-01-01
#>   date_admission   outcome date_outcome date_first_contact date_last_contact
#> 1           <NA> recovered         <NA>               <NA>              <NA>
#> 2           <NA> recovered         <NA>         2022-12-28        2023-01-06
#> 3           <NA> recovered         <NA>         2022-12-29        2023-01-02
#> 4           <NA> recovered         <NA>         2022-12-31        2023-01-05
#> 5           <NA> recovered         <NA>         2022-12-30        2023-01-03
#> 6           <NA> recovered         <NA>         2022-12-31        2023-01-07
#>   ct_value
#> 1       NA
#> 2     24.6
#> 3     24.0
#> 4     26.3
#> 5     27.0
#> 6       NA

To alter these probabilities, supply a named vector to the sim_linelist() argument case_type_probs. The vector should contain three numbers, with the names suspected, probable and confirmed, with the numbers summing to one. Here we change the values to simulate an outbreak in which the proportion of cases confirmed by laboratory testing is high.

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,
  case_type_probs = c(suspected = 0.05, probable = 0.05, confirmed = 0.9)
)
head(linelist)
#>   id         case_name case_type sex age date_onset date_reporting
#> 1  1        Kyle Avery confirmed   m  63 2023-01-01     2023-01-01
#> 2  2  Alexandra Walson confirmed   f  43 2023-01-01     2023-01-01
#> 3  4  Elizabeth Costra confirmed   f  17 2023-01-01     2023-01-01
#> 4  7    Rachael Bulwan confirmed   f  81 2023-01-01     2023-01-01
#> 5  8      Cole Stanton confirmed   m  41 2023-01-01     2023-01-01
#> 6  9 Mukhtaar el-Salem confirmed   m   8 2023-01-01     2023-01-01
#>   date_admission   outcome date_outcome date_first_contact date_last_contact
#> 1     2023-01-05      died   2023-02-18               <NA>              <NA>
#> 2           <NA> recovered         <NA>         2022-12-27        2023-01-04
#> 3           <NA> recovered         <NA>         2022-12-26        2023-01-03
#> 4           <NA> recovered         <NA>         2022-12-28        2023-01-03
#> 5           <NA> recovered         <NA>         2022-12-28        2023-01-03
#> 6           <NA> recovered         <NA>         2022-12-26        2023-01-03
#>   ct_value
#> 1     28.8
#> 2     23.3
#> 3     21.6
#> 4     22.6
#> 5     25.2
#> 6     27.8

It is also possible to set one of these categories to 1, in which case every case will be of that type.

The way {simulist} assigns case types is by pasting case types onto existing case data. Thus, it could be viewed that the true underlying data is that all cases in the simulation are confirmed, but that there is a lack of information in some cases. There are no cases in the output line list that are incorrectly attributed as probable or suspected that have not been infected. That is to say, all individuals in the line list, whatever their case type, are infected during the outbreak.

Anonymous line list

By default sim_linelist() provides the name of each individual in the line list. If an anonymised line list is required the anonymise argument of sim_linelist() can be set to TRUE.

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,
  anonymise = TRUE
)
head(linelist)
#>   id  case_name case_type sex age date_onset date_reporting date_admission
#> 1  1 NhP49FIptg  probable   m  51 2023-01-01     2023-01-01     2023-01-04
#> 2  4 OtN8E7YVtZ confirmed   m  34 2023-01-01     2023-01-01           <NA>
#> 3  6 YDp6rBKNAl confirmed   f   1 2023-01-01     2023-01-01           <NA>
#> 4  7 vn6Q9OL6nl  probable   m   1 2023-01-01     2023-01-01           <NA>
#> 5  9 GPzXGGMgWr  probable   m  51 2023-01-01     2023-01-01           <NA>
#> 6 11 me7bmYCvSj  probable   f  89 2023-01-02     2023-01-02           <NA>
#>     outcome date_outcome date_first_contact date_last_contact ct_value
#> 1      died   2023-01-22               <NA>              <NA>       NA
#> 2 recovered         <NA>         2022-12-31        2023-01-06     26.6
#> 3 recovered         <NA>         2022-12-31        2023-01-04     25.8
#> 4 recovered         <NA>         2022-12-27        2023-01-05       NA
#> 5 recovered         <NA>         2022-12-29        2023-01-01       NA
#> 6 recovered         <NA>         2022-12-29        2023-01-02       NA

The names used in the line list are produced at random by the {randomNames} R package. Therefore, even when anonymise = FALSE there is no personal data of real individuals being produced or shared. The anonymise argument only changes the $case_name column of the line list, as this is deemed the only personally identifiable information (PII) in the line list data.

Population age

For an overview of how a line list can be simulated with a uniform or structured population age distribution see the vignette dedicated to this topic.

Age-stratified hospitalisation and death risks

For an overview of how a line list can be simulated with age-stratified (or age-dependent) hospitalisation and death risks see the vignette dedicated to this topic.

Simulate contacts table

To simulate a contacts table, the sim_contacts() function can be used. This requires the same arguments as sim_linelist(), but does not require the onset-to-hospitalisation delay and onset-to-death delays.

contacts <- sim_contacts(
  contact_distribution = contact_distribution,
  infectious_period = infectious_period,
  prob_infection = 0.5
)
head(contacts)
#>               from              to age sex date_first_contact date_last_contact
#> 1 Janelle Monarrez   Phillip Degen  51   m         2022-12-29        2023-01-05
#> 2 Janelle Monarrez   Taylor Curtis  17   f         2022-12-30        2023-01-03
#> 3 Janelle Monarrez   Nuzha al-Musa  68   f         2022-12-30        2023-01-02
#> 4    Phillip Degen  Tess Sorghardt  77   f         2022-12-28        2023-01-03
#> 5    Nuzha al-Musa Yutitham Tamura  15   m         2022-12-28        2023-01-06
#> 6    Nuzha al-Musa    Jose Lefever  44   m         2022-12-30        2023-01-03
#>   was_case         status
#> 1        Y           case
#> 2        N under_followup
#> 3        Y           case
#> 4        Y           case
#> 5        N under_followup
#> 6        N under_followup

Simulate both line list and contacts table

To produce both a line list and a contacts table for the same outbreak, the sim_linelist() and sim_contacts() cannot be used separately due to the stochastic algorithm, meaning the data in the line list will be discordant with the contacts table.

In order to simulate a line list and a contacts table of the same outbreak the sim_outbreak() function is required. This will simulate a single outbreak and return a line list and a contacts table. The inputs of sim_outbreak() are the same as the inputs required for sim_linelist().

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$linelist)
#>   id      case_name case_type sex age date_onset date_reporting date_admission
#> 1  1 Brenden Newman  probable   m   6 2023-01-01     2023-01-01           <NA>
#> 2  2  Mayra Montoya confirmed   f  46 2023-01-02     2023-01-02           <NA>
#> 3  4 April Hemphill  probable   f  67 2023-01-03     2023-01-03           <NA>
#> 4  6     Olivia Kim confirmed   f  90 2023-01-04     2023-01-04           <NA>
#> 5  7   Semaj Turner confirmed   m  47 2023-01-02     2023-01-02           <NA>
#> 6  9    Alex Yazzie confirmed   m  65 2023-01-03     2023-01-03           <NA>
#>     outcome date_outcome date_first_contact date_last_contact ct_value
#> 1 recovered         <NA>               <NA>              <NA>       NA
#> 2 recovered         <NA>         2022-12-29        2023-01-04     24.3
#> 3 recovered         <NA>         2022-12-28        2023-01-03       NA
#> 4 recovered         <NA>         2022-12-29        2023-01-04     26.4
#> 5 recovered         <NA>         2022-12-30        2023-01-03     27.6
#> 6 recovered         <NA>         2022-12-27        2023-01-06     25.0
head(outbreak$contacts)
#>             from              to age sex date_first_contact date_last_contact
#> 1 Brenden Newman   Mayra Montoya  46   f         2022-12-29        2023-01-04
#> 2 Brenden Newman Mikaela Gurreri  29   f         2022-12-28        2023-01-02
#> 3 Brenden Newman  April Hemphill  67   f         2022-12-28        2023-01-03
#> 4 Brenden Newman     Clint Rippy  55   m         2022-12-30        2023-01-04
#> 5 Brenden Newman      Olivia Kim  90   f         2022-12-29        2023-01-04
#> 6 Brenden Newman    Semaj Turner  47   m         2022-12-30        2023-01-03
#>   was_case         status
#> 1        Y           case
#> 2        N under_followup
#> 3        Y           case
#> 4        N under_followup
#> 5        Y           case
#> 6        Y           case

sim_outbreak() has the same features as sim_linelist() and sim_contacts(), this includes simulating with age-stratified risks of hospitalisation and death, the probability of case types or contact tracing status can be modified.

Advanced

The sim_*() functions, by default, use an excess degree distribution to account for a network effect when sampling the number of contacts in the simulation model ($q(n) \sim (n + 1)p(n + 1)$ where $p(n)$ is the probability density function of a distribution, e.g., Poisson or Negative binomial, within the .sim_network_bp() internal function). This network effect can be turned off by using the config argument in any sim_*() function and setting network = "unadjusted" (create_config(network = "unadjusted")) which will instead sample from a probability distribution $p(n)$.

Using functions for distributions instead of `<epiparameter>`

The contact_distribution, infectious_period, onset_to_hosp, onset_to_death and onset_to_recovery arguments can accept either an <epiparameter> object (as shown above), or can accept a function. It is possible to use a predefined function or an anonymous function. Here we’ll demonstrate how to use both.

Predefined functions

contact_distribution <- function(x) dpois(x = x, lambda = 2)
infectious_period <- function(x) rgamma(n = x, shape = 2, scale = 2)
onset_to_hosp <- function(x) rlnorm(n = x, meanlog = 1.5, sdlog = 0.5)
onset_to_death <- function(x) rweibull(n = x, shape = 1, scale = 5)

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$linelist)
#>   id       case_name case_type sex age date_onset date_reporting date_admission
#> 1  1 Andrew Terrazas confirmed   m  16 2023-01-01     2023-01-01     2023-01-06
#> 2  2    Max Moraitis confirmed   m  81 2023-01-04     2023-01-04           <NA>
#> 3  3   Brandi Chavez  probable   f  49 2023-01-02     2023-01-02           <NA>
#> 4  5   Glenna Talley confirmed   f   5 2023-01-06     2023-01-06           <NA>
#> 5  8 Nikhil Manglona  probable   m  74 2023-01-07     2023-01-07     2023-01-13
#> 6 10   Alisiana Witt confirmed   f  34 2023-01-08     2023-01-08           <NA>
#>     outcome date_outcome date_first_contact date_last_contact ct_value
#> 1      died   2023-01-06               <NA>              <NA>     23.6
#> 2 recovered         <NA>         2023-01-01        2023-01-03     23.3
#> 3 recovered         <NA>         2022-12-29        2023-01-08       NA
#> 4 recovered         <NA>         2022-12-30        2023-01-07     24.5
#> 5 recovered         <NA>         2022-12-31        2023-01-09       NA
#> 6 recovered         <NA>         2023-01-04        2023-01-12     23.3
head(outbreak$contacts)
#>              from            to age sex date_first_contact date_last_contact
#> 1 Andrew Terrazas  Max Moraitis  81   m         2023-01-01        2023-01-03
#> 2 Andrew Terrazas Brandi Chavez  49   f         2022-12-29        2023-01-08
#> 3    Max Moraitis Kyana Proctor  31   f         2023-01-04        2023-01-06
#> 4    Max Moraitis Glenna Talley   5   f         2022-12-30        2023-01-07
#> 5   Brandi Chavez Devin Mueller  46   f         2022-12-30        2023-01-03
#> 6   Brandi Chavez    Matthew Le  89   m         2023-01-02        2023-01-04
#>   was_case         status
#> 1        Y           case
#> 2        Y           case
#> 3        N under_followup
#> 4        Y           case
#> 5        N under_followup
#> 6        N under_followup

Anonymous functions

outbreak <- sim_outbreak(
  contact_distribution = function(x) dpois(x = x, lambda = 2),
  infectious_period = function(x) rgamma(n = x, shape = 2, scale = 2),
  prob_infection = 0.5,
  onset_to_hosp = function(x) rlnorm(n = x, meanlog = 1.5, sdlog = 0.5),
  onset_to_death = function(x) rweibull(n = x, shape = 1, scale = 5)
)
head(outbreak$linelist)
#>   id             case_name case_type sex age date_onset date_reporting
#> 1  1          Alvina Thesz confirmed   f  24 2023-01-01     2023-01-01
#> 2  4         Marisa Sjoden confirmed   f  40 2023-01-01     2023-01-01
#> 3  8      Joseph Bandreddi confirmed   m  46 2023-01-02     2023-01-02
#> 4  9          Summer Godoy  probable   f  62 2023-01-06     2023-01-06
#> 5 12 Paola Mendez-Martinez confirmed   f  63 2023-01-06     2023-01-06
#> 6 13           Rian Miller  probable   m  83 2023-01-06     2023-01-06
#>   date_admission   outcome date_outcome date_first_contact date_last_contact
#> 1           <NA> recovered         <NA>               <NA>              <NA>
#> 2           <NA> recovered         <NA>         2022-12-26        2023-01-06
#> 3           <NA> recovered         <NA>         2022-12-27        2023-01-04
#> 4     2023-01-22      died   2023-01-25         2022-12-28        2023-01-04
#> 5     2023-01-09      died   2023-01-12         2023-01-04        2023-01-13
#> 6           <NA> recovered         <NA>         2023-01-05        2023-01-08
#>   ct_value
#> 1     25.4
#> 2     25.3
#> 3     25.3
#> 4       NA
#> 5     24.6
#> 6       NA
head(outbreak$contacts)
#>            from              to age sex date_first_contact date_last_contact
#> 1  Alvina Thesz     Taraya Webb   6   f         2022-12-31        2023-01-04
#> 2  Alvina Thesz  James Anderson  71   m         2022-12-27        2023-01-06
#> 3  Alvina Thesz   Marisa Sjoden  40   f         2022-12-26        2023-01-06
#> 4  Alvina Thesz Meghanne Matano  27   f         2022-12-28        2023-01-05
#> 5 Marisa Sjoden   Shelley Reese  27   m         2022-12-30        2023-01-01
#> 6 Marisa Sjoden Elizabeth Weger  45   f         2022-12-28        2023-01-06
#>   was_case           status
#> 1        N   under_followup
#> 2        N   under_followup
#> 3        Y             case
#> 4        N   under_followup
#> 5        N   under_followup
#> 6        N lost_to_followup

The contact_distribution requires a density function instead of a random number generation function (i.e. dpois() or dnbinom() instead of rpois() or rnbinom()). This is due to the branching process simulation adjusting the sampling of contacts to take into account the random network effect.

The same approach of using anonymous functions can be used in sim_linelist() and sim_contacts().

Simulating without hospitalisations and/or deaths

The onset-to-hospitalisation (onset_to_hosp) and onset-to-death (onset_to_death) arguments can also be set to NULL in which case the date of admission ($date_admission) and date of death ($date_death) column in the line list will contains NAs.

linelist <- sim_linelist(
  contact_distribution = contact_distribution,
  infectious_period = infectious_period,
  prob_infection = 0.5,
  onset_to_hosp = NULL,
  onset_to_death = NULL,
  hosp_risk = NULL,
  hosp_death_risk = NULL,
  non_hosp_death_risk = NULL
)
head(linelist)
#>   id         case_name case_type sex age date_onset date_reporting
#> 1  1   Christina Mally  probable   f   9 2023-01-01     2023-01-01
#> 2  2      Travis Reese confirmed   m  82 2023-01-03     2023-01-03
#> 3  4   Cheyanna Madrid confirmed   f  54 2023-01-06     2023-01-06
#> 4  5 Mukarram el-Fadel  probable   m  36 2023-01-03     2023-01-03
#> 5  7   Melani Cardenas confirmed   f  80 2023-01-05     2023-01-05
#> 6  8    Rachael Tharpe  probable   f  75 2023-01-08     2023-01-08
#>   date_admission   outcome date_outcome date_first_contact date_last_contact
#> 1           <NA> recovered         <NA>               <NA>              <NA>
#> 2           <NA> recovered         <NA>         2022-12-30        2023-01-02
#> 3           <NA> recovered         <NA>         2022-12-28        2023-01-06
#> 4           <NA> recovered         <NA>         2023-01-02        2023-01-06
#> 5           <NA> recovered         <NA>         2022-12-31        2023-01-04
#> 6           <NA> recovered         <NA>         2022-12-29        2023-01-05
#>   ct_value
#> 1       NA
#> 2     22.7
#> 3     24.7
#> 4       NA
#> 5     23.6
#> 6       NA

This same functionality also applies to sim_outbreak(). In the above example, hosp_risk, hosp_death_risk and non_hosp_death_risk are set to NULL. If the risk (*_risk) arguments are left as numeric inputs but the corresponding onset-to-event distribution (i.e. hosp_risk for onset_to_hosp and hosp_death_risk and non_hosp_death_risk for onset_to_death) are set to NULL a warning will be produced. The example above simulates with neither hospitalisation or deaths, but these do not need to be turned off together, and one or the other can be set to NULL with their corresponding risk arguments.