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
# get onset to hospital admission from {epiparameter} database
onset_to_hosp <- epiparameter_db(
disease = "COVID-19",
epi_name = "onset to hospitalisation",
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
# 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' functionThe 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.
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 Rafael Diaz confirmed m 39 2023-01-01 <NA> recovered
#> 2 3 Fabian Griffith confirmed m 90 2023-01-01 <NA> recovered
#> 3 4 Annabelle Vu probable f 9 2023-01-01 2023-01-07 recovered
#> 4 5 Emily Fu confirmed f 71 2023-01-01 2023-01-01 died
#> 5 6 Kirsten Barna suspected f 48 2023-01-02 <NA> recovered
#> 6 7 Rajab el-Badie suspected m 77 2023-01-01 <NA> recovered
#> date_outcome date_first_contact date_last_contact ct_value
#> 1 <NA> <NA> <NA> 25.4
#> 2 <NA> 2022-12-31 2023-01-03 21.9
#> 3 <NA> 2022-12-29 2023-01-02 NA
#> 4 2023-02-24 2023-01-02 2023-01-07 22.7
#> 5 <NA> 2022-12-30 2023-01-03 NA
#> 6 <NA> 2022-12-26 2023-01-02 NAThe 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_admission outcome
#> 1 1 Justin Affricano confirmed m 87 2023-01-01 <NA> recovered
#> 2 2 Megan Mccormick probable f 61 2023-01-01 <NA> recovered
#> 3 3 Samuel Griego confirmed m 29 2023-01-01 <NA> recovered
#> 4 4 Trevon Mitchell probable m 71 2023-01-01 2023-02-04 died
#> 5 5 Cheyenne Tom confirmed f 23 2023-01-01 2023-02-07 recovered
#> 6 6 Daejha Buggs confirmed f 7 2023-01-01 <NA> recovered
#> date_outcome date_first_contact date_last_contact ct_value
#> 1 <NA> <NA> <NA> 22.7
#> 2 <NA> 2023-01-03 2023-01-03 NA
#> 3 <NA> 2022-12-31 2023-01-07 24.4
#> 4 2023-01-14 2022-12-25 2023-01-02 NA
#> 5 <NA> 2023-01-04 2023-01-05 24.2
#> 6 <NA> 2023-01-01 2023-01-04 27.0The 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.
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_admission outcome
#> 1 1 Muslim el-Rad probable m 40 2023-01-01 <NA> died
#> 2 3 Lindsay Daw confirmed f 41 2023-01-01 2023-01-04 died
#> 3 4 Margaret Vanovski confirmed f 15 2023-01-01 <NA> recovered
#> 4 5 Billy Vasquez confirmed m 9 2023-01-01 <NA> recovered
#> 5 7 Michaela Olson confirmed f 53 2023-01-02 <NA> recovered
#> 6 9 Julio Gonzalez Mora suspected m 58 2023-01-01 <NA> recovered
#> date_outcome date_first_contact date_last_contact ct_value
#> 1 2023-01-10 <NA> <NA> NA
#> 2 2023-01-17 2023-01-02 2023-01-04 21.6
#> 3 <NA> 2022-12-31 2023-01-06 24.6
#> 4 <NA> 2022-12-31 2023-01-05 24.0
#> 5 <NA> 2022-12-29 2023-01-02 26.3
#> 6 <NA> 2023-01-01 2023-01-03 NATo 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_admission outcome
#> 1 1 Margarita Perry confirmed f 43 2023-01-01 <NA> died
#> 2 2 Tyler Lowe probable m 8 2023-01-01 <NA> recovered
#> 3 6 Hannah Wilmore confirmed f 90 2023-01-01 2023-01-05 recovered
#> 4 7 Vanessa Harris confirmed f 45 2023-01-01 <NA> recovered
#> 5 8 Malik Fuller confirmed m 85 2023-01-01 <NA> recovered
#> 6 9 Charmaine Mitchell confirmed f 18 2023-01-01 <NA> recovered
#> date_outcome date_first_contact date_last_contact ct_value
#> 1 2023-01-07 <NA> <NA> 24.8
#> 2 <NA> 2022-12-31 2023-01-03 NA
#> 3 <NA> 2023-01-01 2023-01-04 22.8
#> 4 <NA> 2022-12-31 2023-01-04 23.6
#> 5 <NA> 2022-12-31 2023-01-02 26.4
#> 6 <NA> 2023-01-05 2023-01-05 26.3It 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.
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_admission outcome
#> 1 1 cClWJSQgqZ confirmed m 46 2023-01-01 <NA> recovered
#> 2 3 zmepWU7Zuu confirmed f 25 2023-01-02 2023-01-13 recovered
#> 3 4 BqNMpEC7MY confirmed m 28 2023-01-02 <NA> recovered
#> 4 6 I5bc1CGQvs confirmed m 6 2023-01-03 <NA> recovered
#> 5 8 EZ3IlginCk confirmed m 26 2023-01-02 <NA> recovered
#> 6 12 S2bqfFpMmt probable f 72 2023-01-05 <NA> recovered
#> date_outcome date_first_contact date_last_contact ct_value
#> 1 <NA> <NA> <NA> 23.8
#> 2 <NA> 2023-01-01 2023-01-02 25.8
#> 3 <NA> 2023-01-01 2023-01-04 20.8
#> 4 <NA> 2023-01-01 2023-01-05 26.6
#> 5 <NA> 2023-01-02 2023-01-03 26.4
#> 6 <NA> 2023-01-04 2023-01-07 NAThe 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.
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.
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.
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
#> 1 Mariah Williams Aric Verde 49 m 2023-01-01
#> 2 Aric Verde Muammar el-Rayes 22 m 2023-01-06
#> 3 Aric Verde Alexis Martinez 88 f 2023-01-04
#> 4 Muammar el-Rayes Deangelo Moody 32 m 2023-01-02
#> 5 Muammar el-Rayes Jamison Bennett 38 m 2023-01-05
#> 6 Muammar el-Rayes Brittanny Owston 63 f 2023-01-04
#> date_last_contact was_case status
#> 1 2023-01-04 Y case
#> 2 2023-01-07 Y case
#> 3 2023-01-04 Y case
#> 4 2023-01-05 N under_followup
#> 5 2023-01-07 N lost_to_followup
#> 6 2023-01-05 Y caseTo 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_admission outcome
#> 1 1 Alyssa Morgan suspected f 51 2023-01-01 <NA> recovered
#> 2 2 Ryan Cline probable f 15 2023-01-01 <NA> recovered
#> 3 4 Brittany Fleddermann confirmed f 47 2023-01-02 <NA> recovered
#> 4 6 Urian Arechiga suspected m 60 2023-01-01 2023-02-24 recovered
#> 5 7 Marwa el-Riaz probable f 43 2023-01-01 <NA> recovered
#> 6 8 Marie Cira suspected f 34 2023-01-02 <NA> recovered
#> date_outcome date_first_contact date_last_contact ct_value
#> 1 <NA> <NA> <NA> NA
#> 2 <NA> 2022-12-30 2023-01-02 NA
#> 3 <NA> 2023-01-01 2023-01-04 24.2
#> 4 <NA> 2022-12-28 2023-01-01 NA
#> 5 <NA> 2023-01-05 2023-01-07 NA
#> 6 <NA> 2023-01-04 2023-01-05 NA
head(outbreak$contacts)
#> from to age sex date_first_contact
#> 1 Alyssa Morgan Ryan Cline 15 f 2022-12-30
#> 2 Ryan Cline Naadir al-Yamin 60 m 2022-12-28
#> 3 Ryan Cline Brittany Fleddermann 47 f 2023-01-01
#> 4 Ryan Cline Brendan Dizon 47 m 2023-01-03
#> 5 Ryan Cline Urian Arechiga 60 m 2022-12-28
#> 6 Ryan Cline Marwa el-Riaz 43 f 2023-01-05
#> date_last_contact was_case status
#> 1 2023-01-02 Y case
#> 2 2023-01-02 N under_followup
#> 3 2023-01-04 Y case
#> 4 2023-01-04 N under_followup
#> 5 2023-01-01 Y case
#> 6 2023-01-07 Y casesim_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) ∼ (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).
<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.
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 = 0.5, scale = 0.2)
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
#> 1 1 Marco Barrios probable m 84 2023-01-01 2023-01-06
#> 2 3 Nicholas Roberts confirmed m 44 2023-01-01 <NA>
#> 3 4 Alec Gonzalez probable m 19 2023-01-01 <NA>
#> 4 5 Kyle Her suspected m 47 2023-01-04 2023-01-10
#> 5 6 Taylor Vazquez-Corrales probable f 13 2023-01-03 <NA>
#> 6 8 Saahira el-Demian confirmed f 66 2023-01-04 <NA>
#> outcome date_outcome date_first_contact date_last_contact ct_value
#> 1 recovered <NA> <NA> <NA> NA
#> 2 recovered <NA> 2022-12-29 2023-01-02 23.4
#> 3 recovered <NA> 2023-01-03 2023-01-05 NA
#> 4 died 2023-01-04 2022-12-29 2023-01-02 NA
#> 5 recovered <NA> 2023-01-01 2023-01-03 NA
#> 6 recovered <NA> 2023-01-01 2023-01-03 24.8
head(outbreak$contacts)
#> from to age sex date_first_contact
#> 1 Marco Barrios Huda al-Younan 67 f 2023-01-03
#> 2 Marco Barrios Nicholas Roberts 44 m 2022-12-29
#> 3 Marco Barrios Alec Gonzalez 19 m 2023-01-03
#> 4 Nicholas Roberts Kyle Her 47 m 2022-12-29
#> 5 Nicholas Roberts Taylor Vazquez-Corrales 13 f 2023-01-01
#> 6 Alec Gonzalez Zahraaa el-Salam 45 f 2023-01-03
#> date_last_contact was_case status
#> 1 2023-01-05 N lost_to_followup
#> 2 2023-01-02 Y case
#> 3 2023-01-05 Y case
#> 4 2023-01-02 Y case
#> 5 2023-01-03 Y case
#> 6 2023-01-06 N under_followupoutbreak <- sim_outbreak(
contact_distribution = function(x) dpois(x = x, lambda = 2),
infectious_period = function(x) rgamma(n = x, shape = 2, scale = 2),
prob_infection = 0.5,
onset_to_hosp = function(x) rlnorm(n = x, meanlog = 1.5, sdlog = 0.5),
onset_to_death = function(x) rweibull(n = x, shape = 0.5, scale = 0.2)
)
head(outbreak$linelist)
#> id case_name case_type sex age date_onset date_admission outcome
#> 1 1 Trae Gurule suspected m 12 2023-01-01 2023-01-11 died
#> 2 3 Quinton Doublin confirmed m 46 2023-01-01 <NA> recovered
#> 3 4 Andres Jacket probable m 35 2023-01-06 <NA> recovered
#> 4 6 Alexander Wartman confirmed m 9 2023-01-02 <NA> recovered
#> 5 7 Miranda Preciado probable f 21 2023-01-05 2023-01-08 died
#> 6 8 Jordon Barnes confirmed m 36 2023-01-05 <NA> recovered
#> date_outcome date_first_contact date_last_contact ct_value
#> 1 2023-01-01 <NA> <NA> NA
#> 2 <NA> 2023-01-04 2023-01-06 26.3
#> 3 <NA> 2022-12-31 2023-01-02 NA
#> 4 <NA> 2023-01-02 2023-01-04 23.0
#> 5 2023-01-05 2023-01-02 2023-01-06 NA
#> 6 <NA> 2023-01-01 2023-01-07 29.2
head(outbreak$contacts)
#> from to age sex date_first_contact
#> 1 Trae Gurule Dillon Peebles 43 m 2023-01-04
#> 2 Trae Gurule Quinton Doublin 46 m 2023-01-04
#> 3 Quinton Doublin Andres Jacket 35 m 2022-12-31
#> 4 Quinton Doublin Paige Guo 73 f 2022-12-28
#> 5 Quinton Doublin Alexander Wartman 9 m 2023-01-02
#> 6 Alexander Wartman Miranda Preciado 21 f 2023-01-02
#> date_last_contact was_case status
#> 1 2023-01-04 N under_followup
#> 2 2023-01-06 Y case
#> 3 2023-01-02 Y case
#> 4 2023-01-02 N under_followup
#> 5 2023-01-04 Y case
#> 6 2023-01-06 Y caseThe contact_distribution requires a density function
instead of a random number generation function
(i.e. dpois() or dnbinom() instead of
rpois() or rnbinom()). This is due to the
branching process simulation adjusting the sampling of contacts to take
into account the random network effect.
The same approach of using anonymous functions can be used in
sim_linelist() and sim_contacts().
The onset-to-hospitalisation (onset_to_hosp) and
onset-to-death (onset_to_death) arguments can also be set
to NULL in which case the date of admission
($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_admission outcome
#> 1 1 Nicolas Xiao probable m 20 2023-01-01 <NA> recovered
#> 2 2 Ashlyn Desalvo confirmed f 90 2023-01-07 <NA> recovered
#> 3 3 Caitlin Gardner confirmed f 45 2023-01-02 <NA> recovered
#> 4 4 Dwayne Cole confirmed m 77 2023-01-08 <NA> recovered
#> 5 6 Brandon Sisson confirmed m 48 2023-01-08 <NA> recovered
#> 6 7 Danielle Linnon confirmed f 4 2023-01-02 <NA> recovered
#> date_outcome date_first_contact date_last_contact ct_value
#> 1 <NA> <NA> <NA> NA
#> 2 <NA> 2023-01-02 2023-01-02 23.4
#> 3 <NA> 2022-12-31 2023-01-03 30.4
#> 4 <NA> 2023-01-08 2023-01-09 23.9
#> 5 <NA> 2023-01-06 2023-01-08 24.2
#> 6 <NA> 2022-12-31 2023-01-04 27.8This 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.