sim_type |
A character string specifying which simulation function
this function is being called within.
|
contact_distribution |
A function or an <epiparameter> object to
generate the number of contacts per infection.
The function can be defined or anonymous. The function must have a single
argument in the form of an integer vector with elements representing the
number of contacts, and return a numeric vector where each element
corresponds to the probability of observing the number of contacts in the
vector passed to the function. The index of the numeric vector returned
is offset by one to the corresponding probability of observing the number
of contacts, i.e. the first element of the output vector is the probability
of observing zero contacts, the second element is the probability of
observing one contact, etc.
An <epiparameter> can be provided. This will be converted into a
probability mass function internally.
The default is an anonymous function with a Poisson probability mass function
(dpois() ) with a mean (\lambda ) of 2 contacts per infection.
|
infectious_period |
A function or an <epiparameter> object for the
infectious period. This defines the duration from becoming infectious to
no longer infectious. In the simulation, individuals are assumed to
become infectious immediately after being infected (the latency period is
assumed to be zero). The time intervals between an infected individual and
their contacts are assumed to be uniformly distributed within the
infectious period. Infectious periods must be strictly positive.
The function can be defined or anonymous. The function must return a vector
of randomly generated real numbers representing sampled infectious periods.
The function must have a single argument, the number of random infectious
periods to generate.
An <epiparameter> can be provided. This will be converted into random
number generator internally.
The default is an anonymous function with a lognormal distribution random
number generator (rlnorm() ) with meanlog = 2 and sdlog = 0.5 .
|
prob_infection |
A single numeric for the probability of a secondary
contact being infected by an infected primary contact.
|
outbreak_start_date |
A date for the start of the outbreak.
|
outbreak_size |
A numeric vector of length 2 defining the minimum and
the maximum number of infected individuals for the simulated outbreak.
Default is c(10, 1e4) , so the minimum outbreak size is 10 infected
individuals, and the maximum outbreak size is 10,000 infected individuals.
Either number can be changed to increase or decrease the maximum or minimum
outbreak size to allow simulating larger or smaller outbreaks. If the
minimum outbreak size cannot be reached after running the simulation for
many iterations (internally) then the function errors, whereas if the
maximum outbreak size is exceeded the function returns the data early and a
warning stating how many cases and contacts are returned.
|
onset_to_hosp |
A function or an <epiparameter> object for the
onset-to-hospitalisation delay distribution. onset_to_hosp can also be
set to NULL to not simulate hospitalisation (admission) dates.
The function can be defined or anonymous. The function must return a vector
of numeric s for the length of the onset-to-hospitalisation delay. The
function must have a single argument.
An <epiparameter> can be provided. This will be converted into a random
number generator internally.
The default is an anonymous function with a lognormal distribution random
number generator (rlnorm() ) with meanlog = 1.5 and sdlog = 0.5 .
If onset_to_hosp is set to NULL then hosp_risk and hosp_death_risk
will be automatically set to NULL if not manually specified.
|
onset_to_death |
A function or an <epiparameter> object for the
onset-to-death delay distribution. onset_to_death can also be set to
NULL to not simulate dates for individuals that died.
The function can be defined or anonymous. The function must return a vector
of numeric s for the length of the onset-to-death delay. The function must
have a single argument.
An <epiparameter> can be provided. This will be converted into a random
number generator internally.
The default is an anonymous function with a lognormal distribution random
number generator (rlnorm() ) with meanlog = 2.5 and sdlog = 0.5 .
If onset_to_death is set to NULL then non_hosp_death_risk and
hosp_death_risk will be automatically set to NULL if not manually
specified.
For hospitalised cases, the function ensures the onset-to-death time is
greater than the onset-to-hospitalisation time. After many (1000) attempts,
if an onset-to-death time (from onset_to_death ) cannot be sampled that is
greater than a onset-to-hospitalisation time (from onset_to_hosp ) then
the function will error. Due to this conditional sampling, the
onset-to-death times in the line list may not resemble the distributional
form input into the function.
|
onset_to_recovery |
A function or an <epiparameter> object for the
onset-to-recovery delay distribution. onset_to_recovery can also be NULL
to not simulate dates for individuals that recovered.
The function can be defined or anonymous. The function must return a vector
of numeric s for the length of the onset-to-recovery delay. The function
must have a single argument.
An <epiparameter> can be provided. This will be converted into a random
number generator internally.
The default is NULL so by default cases that recover get an NA in the
$date_outcome line list column.
For hospitalised cases, the function ensures the onset-to-recovery time is
greater than the onset-to-hospitalisation time. After many (1000) attempts,
if an onset-to-recovery time (from onset_to_recovery ) cannot be sampled
that is greater than a onset-to-hospitalisation time (from onset_to_hosp )
then the function will error. Due to this conditional sampling, the
onset-to-recovery times in the line list may not resemble the distributional
form input into the function.
|
anonymise |
A logical boolean for whether case names should be
anonymised. Default is FALSE .
|
case_type_probs |
A named numeric vector with the probability of
each case type. The names of the vector must be "suspected" , "probable" ,
"confirmed" . Values of each case type must sum to one.
|
contact_tracing_status_probs |
A named numeric vector with the
probability of each contact tracing status. The names of the vector must
be "under_followup" , "lost_to_followup" , "unknown" . Values of each
contact tracing status must sum to one.
|
hosp_risk |
Either a single numeric for the hospitalisation risk of
everyone in the population, or a <data.frame> with age specific
hospitalisation risks. Default is 20% hospitalisation (0.2 ) for the entire
population. If the onset_to_hosp argument is set to NULL this argument
will automatically be set to NULL if not specified or can be manually
set to NULL . See details and examples for more information.
|
hosp_death_risk |
Either a single numeric for the death risk for
hospitalised individuals across the population, or a <data.frame> with age
specific hospitalised death risks Default is 50% death risk in hospitals
(0.5 ) for the entire population. If the onset_to_death argument is set
to NULL this argument will automatically be set to NULL if not specified
or can be manually set to NULL . See details and examples for more
information. The hosp_death_risk can vary through time if specified in
the time_varying_death_risk element of config , see
vignette("time-varying-cfr", package = "simulist") for more information.
|
non_hosp_death_risk |
Either a single numeric for the death risk for
outside of hospitals across the population, or a <data.frame> with age
specific death risks outside of hospitals. Default is 5% death risk outside
of hospitals (0.05 ) for the entire population. If the onset_to_death
argument is set to NULL this argument will automatically be set to NULL
if not specified or can be manually set to NULL . See details and examples
for more information. The non_hosp_death_risk can vary through time if
specified in the time_varying_death_risk element of config , see
vignette("time-varying-cfr", package = "simulist") for more information.
|
population_age |
Either a numeric vector with two elements or a
<data.frame> with age structure in the population. Use a numeric vector
to specific the age range of the population, the first element is the lower
bound for the age range, and and the second is the upper bound for the age
range (both inclusive, i.e. [lower, upper]). The <data.frame> with
age groups and the proportion of the population in that group. See details
and examples for more information.
|
Arguments that are used by all simulation functions are required
and not given a default value, for other arguments that are not inputs in
all simulation functions a default of NULL
is used.
Defaults mentioned in argument documentation is the default for the exported
simulation function and not the default in this checking function. In this
function there is either no default or NULL
.